[ANALYSIS_REPORT] **POOR_FIT** [Copilot: claude-sonnet-4] Critical domain mismatch - candidate has strong systems/AI/cloud infrastructure background but zero embedded systems or C++ experience. Missing all core firmware engineering requirements despite being a fast learner. **Critical Gaps:** - Embedded systems development - completely different from cloud/AI infrastructure - C/C++ programming - candidate only has Python experience - Hardware-level programming - candidate works at application/infrastructure layer **Strengths:** - English fluency (advanced) - Remote work capability - Systems thinking and architecture **Missing Required:** C++ expertise, Embedded Linux experience, Embedded systems knowledge, Hardware optimization, Communication protocols (UART/SPI/I2C), Firmware development lifecycle

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate presents a very strong technical background in AI infrastructure, systems architecture, and automation. However, there are critical gaps in direct experience with content production, social media, entertainment, and the specific technical skills required for video editing and post planning, which are central to this educational role. **Critical Gaps:** - Experience in entertainment, social media, or content production - Video editing, post planning, and digital culture (in content creation context) **Strengths:** - Strong foundation in AI infrastructure and system architecture - Proven ability to build complex, automated systems (e.g., JIT inventory, LLM clusters) - Demonstrated fast-learning capabilities and self-education **Missing Required:** Experiência comprovada no mercado de entretenimento, redes sociais ou produção de conteúdo, Operar e ensinar edição de vídeo (básico/intermediário), Operar e ensinar planejamento de posts, Operar e ensinar cultura digital

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Flash] The candidate presents strong backend, infrastructure, and automation skills, including expertise in Python, SQL, Docker, CI/CD, and API design, demonstrating a proven ability to build complex production systems. However, there are critical gaps in core frontend technologies (React, Next.js, and their ecosystem) and a fundamental absence of TypeScript experience. The explicit requirement for Node.js as the primary backend language is also a significant missing skill. Despite these deficiencies, the candidate's 'fast learner' track record and deep system-building experience suggest potential for growth. **Critical Gaps:** - Frontend Development (React, Next.js, UI componentization ecosystem) - TypeScript **Strengths:** - Strong Backend, Infrastructure, and DevOps foundation (Docker, Git, Linux, SQL, CI/CD, API Design). - Proven ability to build complex production systems from scratch, demonstrating deep technical understanding and autonomy. - Exceptional 'Fast Learner' capability with a clear career trajectory towards advanced technical roles and AI. **Missing Required:** Node.js (as primary backend language)

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate presents a strong profile as an AI Infrastructure Strategist and Solution Architect with over 10 years of experience, specializing in Python, Docker, and complex system design. However, there are critical gaps in the required core technologies for a 'Jr Full Stack Developer (React + Java)' role, including explicit experience in React, TypeScript, HTML, CSS, Java 11+, and the Spring Framework. Furthermore, the candidate's senior-level experience and career trajectory are fundamentally misaligned with a junior-level full-stack position. **Critical Gaps:** - Frontend Development Stack (React, TypeScript, HTML, CSS) - Backend Development Stack (Java 11+, Spring Framework) - Experience Level (Jr Developer vs. Architect/Founder with 10+ years) **Strengths:** - Extensive System Architecture and Design Expertise - Strong proficiency in AI Infrastructure (self-hosted LLMs, PyTorch) and Python development - Proven ability as a fast learner and adaptable technologist with a continuous education track record **Missing Required:** Oracle (specific database experience), GCP (specific cloud platform experience), Messaging (RabbitMQ, Kafka or similar), Kubernetes, TDD (Test Driven Development) and Unit tests

[ANALYSIS_REPORT] **DISQUALIFIED** [G2.5 Flash] The candidate presents a strong technical background with significant experience in system architecture, AI infrastructure, and project management. However, there is a critical and blocking gap in specific Odoo experience and demonstrated end-to-end ERP implementation project management. Furthermore, the candidate's general location in Brazil conflicts with the job's explicit requirement for on-site presence in Grande SP/Guarulhos, leading to disqualification. **Critical Gaps:** - Odoo (specific ERP platform) - Geographic availability (on-site in Guarulhos/SP) **Strengths:** - Proven Fast Learner and Self-Starter - Strong General Project Management Abilities - Robust Technical/AI Infrastructure Background **Missing Required:** Odoo experience, ERP implementation project management experience

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate demonstrates strong architectural and system design capabilities, with proven experience in building automated production systems and CI/CD pipelines. However, there's a critical gap in the primary programming language (Go/Golang) and a lack of specific observability tools and messaging queues required for the role. **⚠️ Critical Gaps:** - Go (Golang) - specified as the principal language **✅ Strengths:** - Strong System Architecture & Clean Architecture principles - Expertise in Automation & CI/CD - Proven ability to learn and deploy complex technologies **❌ Missing Required:** Go (Golang), Observability (Prometheus, OpenTelemetry, Sentry, Grafana), RabbitMQ

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Flash] The candidate presents a strong foundation in systems architecture, automation, and CI/CD, with demonstrated experience building production-grade systems and complex infrastructure. There's a notable gap in explicitly demonstrated solid, hands-on experience with specific AWS services at scale for high-availability environments, although foundational AWS knowledge and inference of capability exist. **✅ Strengths:** - Exceptional ability to architect, automate, and deploy complex production systems. - Strong proficiency in core DevOps tools including Terraform, Docker, CI/CD, Git, Python, and Linux. - Proven fast learner with an intentional career progression towards technical and AI infrastructure roles. **❌ Missing Required:** Demonstrated solid experience operating and evolving distributed, critical, and high-availability environments specifically on AWS, Explicit hands-on experience with Prometheus/Grafana

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-4.1] O candidato demonstra forte experiência em arquitetura de sistemas, automação, Python, SQL, infraestrutura local e fundamentos de cloud, além de liderança técnica comprovada. No entanto, faltam menções explícitas a Databricks, Spark, Azure, orquestradores de dados (Airflow/Datafactory), e Big Data streaming (Kafka/Flink), que são requisitos centrais para a vaga. A base técnica é sólida e o perfil de aprendizado rápido é evidente, mas há lacunas em ferramentas específicas do ecossistema moderno de dados. **⚠️ Critical Gaps:** - Databricks - Apache Spark - Azure Cloud **✅ Strengths:** - Python e automação avançada - Experiência em arquitetura de sistemas e liderança técnica - Governança e segurança de dados **❌ Missing Required:** Databricks, Apache Spark, Azure Cloud

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate demonstrates strong foundational software engineering skills, architectural experience, and a proven fast-learner ability across various domains (Python, Docker, AI infrastructure). However, there's a critical and broad mismatch with the job's core requirement for Microsoft FullStack development, including C#, .NET, Angular, SQL Server, and the Azure ecosystem. **⚠️ Critical Gaps:** - Proficiency in Microsoft FullStack Development (C#, .NET, Angular, SQL Server, Azure Ecosystem, Visual Studio, Azure DevOps) **✅ Strengths:** - Strong foundational software engineering, architecture, and automation skills (Python, Docker, Git, CI/CD). - Proven track record of building and deploying complex production systems, demonstrating strong engineering acumen. - Exceptional capacity for self-learning and adapting to new complex technologies (e.g., self-hosted LLM clusters, algorithmic trading). **❌ Missing Required:** Kubernetes

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate demonstrates strong infrastructure, systems architecture, and Python skills with a proven fast-learner track record. However, there are critical gaps in the primary required technologies: Java/Spring for backend and modern frontend frameworks (like React). While many soft skills and some API/corporate system experience match, the fundamental technical stack mismatch is significant. **⚠️ Critical Gaps:** - Java/Spring backend ecosystem - Modern frontend framework experience (e.g., React) **✅ Strengths:** - Strong systems architecture and design capabilities - Proven experience with full-lifecycle project ownership (from concept to production) - Fast learner with extensive self-taught technical skills and continuous education **❌ Missing Required:** Java 8+, Spring / Spring Boot, Experiência com framework frontend moderno (ex: React), Consumo de APIs REST (no contexto frontend), Construção de interfaces administrativas / operacionais, Noções de UX para sistemas corporativos

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Flash] The candidate demonstrates exceptional expertise in AI infrastructure, backend systems, and data management with a strong track record of building complex, automated solutions. However, there are significant critical gaps in the core Full Stack frontend technologies (Next.js, tRPC, Tailwind, shadcn-ui) and the required strong emphasis on the JavaScript/TypeScript ecosystem for this role, which pull the score down considerably. **⚠️ Critical Gaps:** - Frontend Web Development (Next.js, Tailwind, shadcn-ui) - Strong emphasis on JavaScript/TypeScript ecosystem for Full Stack role **✅ Strengths:** - AI Infrastructure & Integration (self-hosted LLMs, AI trading bots) - Systems Architecture & Backend Engineering (JIT systems, clean architecture) - Data Management (SQL, Pandas/NumPy) & Automation (Python, shell scripting) - Exceptional Fast Learner (self-taught AI, trading, continuous education) - Analytical Problem Solver & Project Leadership **❌ Missing Required:** Next.js (App Router), TypeScript (as primary Full Stack development language), Drizzle ORM, tRPC, Habilidade na construção de interfaces complexas e responsivas

[ANALYSIS_REPORT] **STRONG_MATCH** [Copilot: claude-sonnet-4.5] Strong foundational match with demonstrated backend engineering experience. Candidate shows Python expertise, SQL proficiency, and systems architecture capabilities that align well with core requirements. The 10+ years of building production systems (JIT inventory, automation pipelines, trading bots) demonstrates backend engineering competency. Engineering degree and proven 'metal-up' architecture approach fit the role's technical demands. Primary gaps are lack of explicit web framework mentions (Django/Flask/FastAPI) and no time-series database experience, though the algorithmic trading background suggests relevant data handling skills. **✅ Strengths:** - Strong Python programming with production systems experience (JIT inventory, trading bots, automation) - SQL database proficiency (Postgres/MySQL) with demonstrated use in complex systems - Systems architecture and OOP principles applied in real-world production environments (10+ years building self-sustaining systems) - Cross-functional collaboration experience (worked with engineering teams, managed stakeholders) - Engineering degree (Mechatronics) + Supply Chain MicroMasters shows technical + domain breadth

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate demonstrates exceptional technical prowess in systems architecture, AI infrastructure, and Python development, coupled with a strong 'fast learner' ability. However, the role critically requires expertise in Node.js and React.js for Full-Stack development, which are absent from the candidate's profile, leading to a significant domain mismatch for the specific technology stack. **⚠️ Critical Gaps:** - Node.js (Required for Full-Stack development) - React.js (Required for Full-Stack development) **✅ Strengths:** - Deep expertise in systems architecture, AI infrastructure, and Python backend development. - Exceptional 'Fast Learner' with demonstrated ability to build complex production systems. - Strong foundation in Git, SQL, Linux, and basic AWS knowledge.

[ANALYSIS_REPORT] **DISQUALIFIED** [G2.5 Flash] While the candidate demonstrates strong foundational technical and infrastructure skills, including a proven fast-learning ability and a clear career trajectory towards technical roles, there's a critical mismatch in the core development stack. The role heavily emphasizes Node.js, JavaScript, TypeScript, and React.js, areas where the candidate has no explicit experience, leading to a significant domain gap despite high scores in other areas. **⚠️ Critical Gaps:** - Frontend JavaScript Development (React.js, hooks, state management) - Backend JavaScript/TypeScript Development (Node.js, Express, NestJS) - Specialized Testing Tools (Jest, React Testing Library) **✅ Strengths:** - Strong infrastructure and DevOps skills (Docker, CI/CD, Git, AWS basics) - Proven ability to architect and build complex automated systems from concept to execution - Exceptional self-learner with a demonstrated track record of acquiring and applying new technical domains **❌ Missing Required:** MongoDB, Azure, Vercel, Agile practices

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5.1] Strong systems, analytics, and workflow skills align with the operations/process side of the role, but there is a significant gap in hands-on content marketing, CMS, and channel-specific experience (email/social/blog/podcast) and SEO/digital marketing fundamentals. **⚠️ Critical Gaps:** - Hands-on content marketing and editorial experience - Experience with content management systems (CMS) such as WordPress, Webflow, or similar - Operational ownership of email, social media, blog, and podcast content calendars - SEO and digital marketing skills directly applied to content optimization **✅ Strengths:** - Process-driven and systems-oriented mindset well suited to building structured content operations and rotations - Strong analytical background to interpret data and optimize performance of programs and workflows - Proven ability to manage complex, recurring workflows and operations independently **❌ Missing Required:** Demonstrated content development and management experience for blog/email/social, Proficiency with content management systems (CMS), Content strategy and workflow management in a marketing or media team, Knowledge of SEO and content optimization best practices, Digital marketing principles applied to lead generation and engagement, Lead and engagement tracking across content channels using marketing analytics tools, Bachelor’s degree in Communications, Marketing, Journalism, or closely related field (resume shows Engineering and Supply Chain)

[ANALYSIS_REPORT] **STRONG_MATCH** [G2.5 Flash] The candidate presents an exceptionally strong profile for this role, leveraging extensive experience in financial markets and cryptocurrency trading to directly address on-chain data analysis and risk management requirements. Their background in architecting automated systems, deploying privacy-first AI solutions, and proficiency in Python and SQL robustly covers data engineering, automation, and security principles. Although specific experience with Hex dashboards is not listed, their proven ability to quickly learn complex technologies and continuously self-educate indicates a high potential for rapid skill acquisition in this area. **✅ Strengths:** - Financial/Crypto Market Expertise & Algorithmic Trading - Data Security Principles & Privacy-focused Systems Architecture - Python & SQL for Data Processing, Automation, and Analytics **❌ Missing Required:** Building Dashboards in Hex (or similar BI tools)

[ANALYSIS_REPORT] **STRONG_MATCH** [G2.5 Flash] The candidate exhibits deep expertise in Python, Machine Learning, and architecting high-performance production systems, evidenced by successful projects like self-hosted AI clusters and automated JIT inventory systems. Their career trajectory and fast-learner profile strongly align with technical roles, showcasing significant hands-on experience with core ML libraries. The main area of concern is the lack of demonstrated experience in competitive programming platforms, a specifically required skill. **✅ Strengths:** - Expert-level Python and ML application in production systems - Strong system design, clean architecture, and performance optimization skills - Exceptional ability to learn and implement complex technologies (fast learner bonus) **❌ Missing Required:** Proven track record on coding platforms (LeetCode, Codeforces, HackerRank, or Kaggle competitions)

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Flash] The candidate presents a strong background in backend systems, AI infrastructure, and demonstrable leadership, aligning well with the AI-first mindset and senior expectations. However, a critical gap in professional React experience, a core requirement for this full-stack role, significantly impacts the overall match. **⚠️ Critical Gaps:** - 3+ years of experience using React **✅ Strengths:** - Strong AI-First Mindset & AI Infrastructure Experience - Extensive Backend, API, Database, and Data Pipeline Expertise - Demonstrated High-Ownership, Fast Learner, and Systems Architecture Acumen **❌ Missing Required:** 3+ years of experience using React

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Flash] The candidate demonstrates exceptional production-grade Python proficiency and significant Machine Learning/AI infrastructure experience, directly aligning with core technical requirements for building high-performance AI and data pipelines. Their background in system design and optimization is also a strong asset. However, a key explicit requirement for proven track record on competitive programming or data challenge platforms is not met, preventing a perfect match. **✅ Strengths:** - Exceptional production-grade Python and ML expertise - Strong system design and performance optimization capabilities - Proven fast learner with an intentional career progression into AI/technical roles **❌ Missing Required:** Proven track record on coding platforms (LeetCode, Codeforces, HackerRank, or Kaggle), AsyncIO proficiency

[ANALYSIS_REPORT] **DISQUALIFIED** [Copilot: claude-sonnet-4.5] Candidate demonstrates strong systems architecture thinking and automation expertise, but lacks the specific production n8n experience that is non-negotiable for this role. While the candidate has built automated systems (JIT inventory, trading bots), there's no evidence of iPaaS platform experience, n8n deployment, or platform migration work. The 3+ years n8n requirement is a hard blocker. **⚠️ Critical Gaps:** - No n8n experience (3+ years required - BLOCKING) - No iPaaS platform migration experience (required - BLOCKING) - No self-hosted n8n deployment experience (required - BLOCKING) **✅ Strengths:** - Strong systems architecture mindset - builds end-to-end automated solutions from scratch - Proven ability to design self-sustaining systems with high reliability (95% uptime) - Technical bridge role experience - connects business logic to infrastructure (relevant for retail operations) **❌ Missing Required:** n8n production experience (3+ years), Self-hosted n8n deployments, iPaaS migration experience (Workato/Zapier/Make), Secure on-premise integration patterns (SSH/VPN/agents), Event-driven automation architectures

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Flash] The candidate presents a strong profile for a Data Scientist role, demonstrating expert-level Python skills in production, significant Machine Learning experience (LLMs, trading bots), and proven ability to build and optimize high-performance, production-ready systems. The primary gap is the lack of competitive programming or data challenge platform experience, which is a key requirement. **✅ Strengths:** - Expert-level Python and Machine Learning experience in production environments. - Proven ability to architect and optimize high-performance, production-grade systems. - Strong understanding of algorithms, data structures, and clean architecture, coupled with a fast learning ability and a clear career trajectory into AI infrastructure. **❌ Missing Required:** Proven track record on coding platforms — e.g., top percentile or strong rating on LeetCode, Codeforces, HackerRank, or Kaggle competitions.

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate is a highly skilled technical architect and engineer with extensive experience in AI and system automation. However, their professional background fundamentally lacks direct experience in core Executive Assistant duties such as administrative support, calendar management, and personal assistance, resulting in a significant mismatch for the role. **⚠️ Critical Gaps:** - Primary professional domain (technical architecture/engineering vs. administrative Executive Assistant) **✅ Strengths:** - Proven 'Fast Learner' with a track record of mastering complex domains. - Highly tech-savvy with deep expertise in AI, strongly aligning with 'tech savvy' and 'uses AI tools' requirements. - Demonstrated initiative and ability to architect and execute complex technical systems. **❌ Missing Required:** Prior Executive Assistant experience, End to end CEO support across business and personal admin, Calendar control and priority management, Communication flow across email, Slack, and social (for CEO), Travel related support and coordination, Personal administration (scheduling personal events, purchasing gifts, personal health tasks), Marketing Support (manual social media posting, podcast support), Mastermind Event Coordinator, Team+Culture Support (communication, reports, gifts, new hire onboarding)

[ANALYSIS_REPORT] **DISQUALIFIED** [G2.5 Flash] The candidate is disqualified due to not meeting the hard filter for the role. The job description explicitly states it is a 12-month internship for current students or recent graduates, which conflicts with the candidate's 10+ years of professional experience and the timeline of their academic degrees. **⚠️ Critical Gaps:** - Does not meet the internship's target audience criteria (current student/recent graduate) - Overqualified for an internship role **✅ Strengths:** - Demonstrated expert-level proficiency in Python and SQL through complex project implementation. - Extensive experience in systems architecture, automation, and AI infrastructure, exceeding internship requirements. - Proven strong analytical and problem-solving skills, coupled with a track record as a fast learner and clear career progression.

[ANALYSIS_REPORT] **DISQUALIFIED** [G2.5 Flash] The candidate possesses a strong technical background in AI infrastructure, systems architecture, and engineering, demonstrating significant expertise in Python, automation, and cloud technologies. However, the Senior Tax Analyst role requires specialized knowledge and experience in direct tax calculation (IRPJ, CSLL), ancillary obligations (ECF), and an educational background in accounting or related financial/legal fields, which are entirely absent from the candidate's profile. This represents a fundamental domain and educational mismatch. **⚠️ Critical Gaps:** - Lack of relevant educational background (Accounting, Law, Administration or related fields for a tax role) - Complete absence of professional experience in tax analysis, compliance, and specific tax obligations (IRPJ, CSLL, ECF) **✅ Strengths:** - Strong background in technical system architecture and AI infrastructure - Proven track record as a fast learner and builder of complex automated systems - Fluent in English and experienced in various technical tools and programming languages **❌ Missing Required:** Conhecimento em apuração de impostos diretos: IRPJ e CSLL, Experiência com entrega de obrigações acessórias, especialmente ECF

[ANALYSIS_REPORT] **DISQUALIFIED** [Copilot: claude-sonnet-4.5] This is a severe domain mismatch. The job requires a licensed accountant (CRC registration) with formal Accounting degree and deep expertise in Brazilian tax accounting (Lucro Real/Presumido, SPED Contábil, biological assets). The candidate is a Systems Architect/Engineer with Mechatronics degree and 10+ years in tech/supply chain. While the candidate has financial analysis experience and Excel skills, they lack the mandatory professional accounting credentials and specialized tax/audit knowledge. This represents a complete career pivot requiring years of re-education and certification. **⚠️ Critical Gaps:** - Mandatory Accounting Degree (has Mechatronics Engineering) - Active CRC Professional Registration (accountant license) - BLOCKING - Brazilian Tax Accounting Expertise (Lucro Real/Presumido, SPED) - Professional Accounting Career Track (candidate is Systems/Engineering focused) **✅ Strengths:** - Excel proficiency from financial analysis background - Organizational skills demonstrated through complex system builds - Some ERP exposure from supply chain role **❌ Missing Required:** Graduação em Ciências Contábeis, Registro ativo no CRC

[ANALYSIS_REPORT] **DISQUALIFIED** [G2.5 Flash] The candidate does not meet the mandatory language requirement of advanced Spanish, which is explicitly stated as obligatory for this project. This results in automatic disqualification. **⚠️ Critical Gaps:** - SAP Basis Security domain expertise - Advanced Spanish language proficiency **❌ Missing Required:** Advanced Spanish, SAP Security and Authorizations Administration, Maintenance of simple, composite, and derived SAP Security profiles, Maintenance of profiles and parameters in SAP environment, Analysis of authorization traces for problem identification and resolution, User and license management with Active Directory integration, Experience in integrations between satellite systems in the steel industry, Creation and maintenance of users and profiles via Middleware Client

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: claude-sonnet-4] Strong foundational match with complementary automation and systems experience. Candidate demonstrates proven ability to architect complex systems and has relevant engineering background, but lacks specific industrial automation tools required for this manufacturing-focused Data Engineer role. **⚠️ Critical Gaps:** - Rockwell FactoryTalk Gateway - explicitly required - RSLinx - explicitly required **✅ Strengths:** - Strong systems architecture and automation experience - Proven track record building production-level data systems - Engineering degree in Mechatronics with automation focus **❌ Missing Required:** Rockwell FactoryTalk Gateway, RSLinx, Manufacturing systems integration experience

[ANALYSIS_REPORT] **STRONG_MATCH** [G2.5 Flash] The candidate presents an exceptionally strong match for this general software developer role, demonstrating extensive and recent experience in core development skills, API integration, and troubleshooting within production environments. Their career trajectory and self-taught expertise in complex domains further enhance their fit. **✅ Strengths:** - Extensive and recent experience in core software development, API integration, and production systems. - Demonstrated ability to self-teach and implement complex, cutting-edge technologies (AI infrastructure, algorithmic trading). - Strong systems architecture and automation experience, directly applicable to building robust software solutions.

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-4.1] The candidate is a strong match for the core requirements: deep Python expertise, Docker, Linux, Bash, SQL, and automation. They have proven experience building complex, reproducible systems and self-hosting LLMs, which aligns well with the role's focus on LLM evaluation and infrastructure. The only notable gap is direct, recent experience with JavaScript/Node.js/TypeScript and explicit use of LLM coding tools (Cursor, Copilot, CodeWhisperer), but their fast-learning profile and history of cross-domain work suggest these are quickly bridgeable. **✅ Strengths:** - Python and automation expertise - Docker and Linux infrastructure - Proven ability to build complex, reproducible systems **❌ Missing Required:** JavaScript/Node.js/TypeScript (explicit, not inferred), LLM coding tools (Cursor, Copilot, CodeWhisperer)

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate demonstrates strong general engineering, automation, and system architecture skills, but has a critical gap in direct, practical experience with UI and API test automation, which is central to this role. While proficient in related areas like CI/CD and general scripting, the specific domain expertise required for a Test Automation Engineer (e.g., test case creation, API testing tools, test frameworks) is notably absent from their resume. Their career trajectory also points away from a dedicated test automation role. **⚠️ Critical Gaps:** - Lack of 3+ years practical experience with UI and API test automation (candidate's automation is in systems/data, not testing) **✅ Strengths:** - Strong general engineering and system automation experience - Proficiency in Python, Git, and CI/CD concepts - Excellent analytical, troubleshooting, and communication skills **❌ Missing Required:** Forte conhecimento em criação e execução de casos de teste, Experiência com metodologias ágeis (Agile), Experiência com ferramentas de teste de API como Postman ou similares, Experiência com frameworks de teste, Microsoft Certificação Automation Engineer ou SDET

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5.1] Forte em arquitetura, automação, Docker, CI/CD, bancos relacionais e cloud básica, mas falta experiência comprovada em JavaScript/TypeScript, Node.js e React, que são o núcleo da vaga FullStack. **⚠️ Critical Gaps:** - Domínio avançado de JavaScript/TypeScript - Experiência sólida em Node.js para backend - Proficiência em React para frontend - Prática concreta com Gherkin BDD e BDD em times de produto - Arquitetura e implementação de microsserviços em produção - Uso sistemático de TDD/BDD em projetos JavaScript/TypeScript **✅ Strengths:** - Arquitetura de sistemas, automação e integração de APIs em ambientes de produção - Domínio de Docker, CI/CD e infraestrutura Linux com foco em resiliência e operação contínua - Experiência sólida com bancos relacionais (PostgreSQL/MySQL) e análise quantitativa/engenharia de dados em Python **❌ Missing Required:** Domínio avançado de JavaScript/TypeScript com forte experiência em Node.js, Proficiência em React e ecossistema (componentes, hooks, gerenciamento de estado), Habilidade e experiência com Histórias de Usuário em Gherkin BDD, Experiência com metodologias de testes TDD/BDD, Experiência explícita com arquitetura de microsserviços, Experiência prática em GraphQL (caso seja exigido além de REST), Experiência robusta em cloud pública focada em aplicações web (AWS/Azure/GCP) além de noções básicas

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate presents strong foundational skills in automation, systems architecture, and reliability engineering, backed by a clear career progression and a fast-learner aptitude. However, the role's core requirement for expertise in specific monitoring platforms like Splunk, LogicMonitor, and AppDynamics is critically missing. This makes the candidate a poor fit for the specialized platform management focus of this particular Observability & Monitoring Specialist position. **✅ Strengths:** - Strong automation and systems architecture experience, especially in production environments. - Proven fast-learner with continuous education and a career trajectory aligned with technical roles. - Demonstrated data-driven mindset and strong collaboration skills. **❌ Missing Required:** Splunk, LogicMonitor, AppDynamics, Advanced Observability & Monitoring Strategy, SLIs/SLOs, Deep-dive technical support during high-priority incidents, Log aggregation (tool-specific experience), Complex dashboards (specific to monitoring tools)

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate demonstrates strong capabilities in AI infrastructure, MLOps, and developing automated production systems, supported by a fast-learner profile and a career trajectory oriented towards AI. However, there is a critical and blocking gap in direct, hands-on experience with ROS/ROS2 and deploying machine learning models specifically on robotic platforms for dexterous manipulation, which is central to this Chief Applied Scientist role. **⚠️ Critical Gaps:** - Hands-on experience with Robot Operating System (ROS/ROS2) and integrating policies within robotic systems - Minimum 7 years experience developing and deploying machine learning models on robotic platforms **✅ Strengths:** - Strong AI Infrastructure and MLOps expertise (Self-hosted LLMs, PyTorch, Linux, GPU clusters) - Proven track record of architecting and deploying complex production-grade automated systems - Demonstrated fast learner with a clear career progression towards advanced technical/AI roles **❌ Missing Required:** Doctorate in a relevant STEM discipline or Master’s degree with equivalent industry experience in robotics or embodied AI, In-depth understanding of contemporary AI architectures (diffusion models, VLM/VLAs)

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: claude-sonnet-4] Strong foundational match with demonstrated AI/ML experience and Python proficiency, but lacks specific Generative AI production experience and cloud platform expertise. Candidate shows excellent learning potential with systems architecture background. **⚠️ Critical Gaps:** - No demonstrated Generative AI project experience - Missing cloud AI platform experience (GCP/Azure/AWS) **✅ Strengths:** - Strong Python programming - Production-grade system architecture - Self-hosted AI experience shows technical depth **❌ Missing Required:** 6-8 years AI/ML experience, 2+ years Generative AI experience, TensorFlow, Deep Learning, NLP, GCP Vertex AI, LangChain/LangGraph, Flask/FastAPI

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-4.1] The candidate demonstrates strong expertise in infrastructure, automation, Python, Docker, Linux, CI/CD, and security, with proven experience building and operating complex, resilient systems. However, there are notable gaps in direct Kubernetes orchestration, advanced observability tooling (Prometheus, Grafana, OTel), and hands-on SRE/DevOps at scale in cloud-native environments. The candidate's fast learning ability and broad technical foundation suggest a high potential to close these gaps quickly. **⚠️ Critical Gaps:** - Kubernetes - Advanced Observability (Prometheus, Grafana, OTel) - Direct SRE/DevOps leadership in large-scale cloud environments **✅ Strengths:** - Automation and scripting (Python, Shell) - Infrastructure architecture and system design - Security and privacy in AI/Cloud **❌ Missing Required:** Kubernetes, Prometheus, Grafana LGTM, OpenTelemetry

[ANALYSIS_REPORT] **STRONG_MATCH** [Copilot: gpt-5-mini] Strong alignment: candidate has proven Python + PyTorch experience, hands-on LLM work, data pipelines (Pandas) and production deployment experience (Docker/CI/CD/Terraform/local AI clusters) with 3+ years in AI-related work; gaps are mostly in cloud-managed MLOps and some ecosystem tools rather than core ML engineering knowledge. **✅ Strengths:** - Hands-on LLM and AI infra experience (self-hosted clusters) - Strong Python + PyTorch and data-processing skills - Proven track record building production-grade, automated systems

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Flash] The candidate demonstrates strong experience in systems architecture, AI infrastructure, and process automation with Python, aligning well with the job's focus on optimizing engineering processes and leveraging AI. However, there are notable gaps in specific traditional engineering tools (AutoCAD, SharePoint) and in developing equipment specification templates, which are critical for a PMO/Engineering role. The candidate's fast learner profile and relevant career trajectory are significant strengths. **⚠️ Critical Gaps:** - Standardized templates for equipment specification **✅ Strengths:** - Strong expertise in AI infrastructure and automation with Python - Proven ability to create and implement engineering standards and procedures - Fast learner with a clear career progression towards technical/AI roles **❌ Missing Required:** AutoCAD, SharePoint, Develop standardized templates for equipment specification

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: claude-sonnet-4.5] The candidate has strong technical and systems architecture skills but lacks the core Project Management credentials and Agentic AI experience that are strictly required. While they demonstrate project execution capability through their e-commerce business, they lack formal PM experience in enterprise environments with cross-functional teams. The 7+ years PM requirement and 1+ year Agentic AI experience are significant gaps. However, the candidate shows strong adaptability, systems thinking, and technical depth that could translate to PM work with proper training. **⚠️ Critical Gaps:** - Agentic AI - Strictly required 1+ years experience, no evidence in resume - 7+ years formal Project Management role - Candidate has engineering/ops background but not PM-titled positions - PM Methodology Expertise - No demonstrated experience with Waterfall, Agile, Scrum, Kanban in formal PM capacity **✅ Strengths:** - Strong systems architecture and end-to-end solution design - demonstrates strategic thinking similar to PM role - Proven track record of autonomous project delivery (e-commerce automation from concept to production) - Excellent technical foundation with Python, Docker, CI/CD - can bridge technical and business discussions effectively **❌ Missing Required:** Bachelor's in Computer Science/Engineering (has Mechatronics - partial match), 7+ years Project Management experience (has engineering/ops, not PM), 1+ years Agentic AI experience (not mentioned in resume), Waterfall methodology expertise, Agile/Scrum/Kanban formal experience, Cross-functional PM team management, External vendor PM management

[ANALYSIS_REPORT] **STRONG_MATCH** [G2.5 Flash] The candidate is a very strong match, possessing extensive project management experience spanning both IT and business domains, exceeding the 7-year requirement. Their background in self-hosted LLMs and AI infrastructure strongly infers experience with Agentic AI. Coupled with a proven 'Fast Learner' track record, excellent adaptability, and a clear career progression towards AI and systems architecture, they align exceptionally well with the role's diverse demands. **✅ Strengths:** - Extensive Project Management experience (8+ years IT/Business) - Strong inferred Agentic AI experience from LLM infrastructure and automation - Exceptional adaptability, fast learner, and career trajectory aligning with technical/AI leadership

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5.1] Strong alignment on engineering background, end-to-end ownership of automation and warehouse-related systems, and project-style work; main gaps are formal industrial equipment implementation experience, explicit project frameworks, and warehouse automation tooling. **✅ Strengths:** - Engineering degree in Mechatronics/Automation directly aligned with the role’s engineering background requirement - Proven experience designing and running automated JIT inventory and logistics systems for warehouse-like operations - Demonstrated end-to-end ownership of complex technical projects, including requirements, implementation, and operational handover

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate possesses strong technical and analytical skills, particularly in systems architecture, AI infrastructure, and data analysis using Python and SQL. However, there's a significant mismatch in the required Go-To-Market (GTM) specific tech stack and direct experience with growth analytics concepts (e.g., GA4, GTM, CRM platforms, product funnels, A/B testing for marketing). While adaptable, the core domain expertise for this role is largely absent. **⚠️ Critical Gaps:** - GTM specific tech stack (e.g., Google Search Console, Google Tag Manager, Microsoft Clarity, CRM platforms, Data Enrichment tools, Outbound email infrastructure) **✅ Strengths:** - Strong analytical and data processing skills (SQL, Python, statistical analysis) - Expertise in systems architecture, automation, and API integrations - Proven fast learner with a track record of building complex technical systems **❌ Missing Required:** 3+ years experience in a data or growth analytics role, Familiarity with data visualization tools (Looker, Tableau, Amplitude), Understanding of product funnels, experimentation frameworks, growth metrics (CAC, LTV, retention), GA4, Google Search Console, Google Tag Manager, Microsoft Clarity, CRM platforms, Data enrichment tools (Clearbit, ZoomInfo), Outbound email infrastructure/warmup tools, Support experimentation (A/B tests)

[ANALYSIS_REPORT] **STRONG_MATCH** [Copilot: gpt-5-mini] Candidate meets the core requirements (3+ years AI/ML work, Python, PyTorch, data processing and model training experience, advanced English) and shows strong systems/infra experience (self-hosted LLMs, Docker, Terraform, CI/CD). Missing primarily cloud MLOps/orchestration and managed model-serving tooling, but demonstrated metal-up architecture and production deployments indicate fast-learning and high likelihood to ramp quickly. **✅ Strengths:** - Hands-on experience self-hosting and optimizing LLMs - Strong Python + PyTorch + data-processing background - Systems/infra architecture and production deployment experience (Docker, Terraform, CI/CD)

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: claude-sonnet-4.5] Strong technical foundation with proven AI/ML infrastructure experience. Candidate demonstrates hands-on ML deployment (self-hosted LLMs, PyTorch), Python proficiency, and data pipeline experience from algorithmic trading. The 'builder' profile aligns well with BairesDev's implementation focus. Primary gaps are in formal ML frameworks (TensorFlow, scikit-learn) and production ML orchestration tools, but the candidate's track record of rapidly learning complex systems and delivering production-grade solutions suggests high adaptability. Age (33) and diverse technical background are assets for a mid-level AI Engineer role. **✅ Strengths:** - Production-grade system builder: Proven track record delivering automated, zero-maintenance systems (JIT inventory, trading bots) - shows ability to deploy ML to production - AI infrastructure hands-on: Self-hosted and optimized LLMs locally, demonstrating practical ML deployment skills and privacy-first AI approach - Strong Python + Data pipelines: Algorithmic trading experience with Pandas/NumPy/PyTorch shows solid foundation for ML data processing and model implementation **❌ Missing Required:** Formal experience with mainstream ML frameworks (TensorFlow/scikit-learn), Documented MLOps pipeline experience, Production model monitoring and versioning

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5-mini] Strong infra and Python/SQL pipeline experience with proven ability to build production systems and learn quickly, but lacks several core Data Engineering platform skills required for immediate contribution (Azure Databricks, Spark/Delta, ADLS, data modeling/governance). **⚠️ Critical Gaps:** - Azure Databricks (including Databricks Jobs and Delta Live Tables) — core execution platform - Apache Spark / PySpark — required for large-scale transformations - Azure Data Lake Storage (ADLS) integration — storage layer coupling with Databricks - Data Mesh implementation & formal data modeling (Dimensional / Data Vault) — architectural/domain ownership **✅ Strengths:** - Proven ability to design and deliver end-to-end production pipelines and automation - Strong infrastructure and deployment skills (Docker, Terraform, Linux, GPU/cluster ops) - Solid Python + SQL background and experience optimizing systems and workflows **❌ Missing Required:** Hands-on Azure Databricks (ADB) experience, Delta Live Tables, Databricks Jobs, Apache Spark / PySpark optimization experience, Integration with ADLS, Demonstrated Data Mesh implementations, Dimensional or Data Vault modeling experience, Documented Big Data production engineering in a Databricks/Azure ecosystem

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate possesses a strong technical background in engineering and system architecture with demonstrated project delivery, an engineering degree, and some industrial experience. However, there is a critical gap in direct experience (7-10 years) as a Project Planning Manager, specifically focusing on formal schedule management for large industrial projects. This, coupled with a lack of specific planning tool knowledge and railway domain expertise, makes the candidate a poor fit for this role despite strong problem-solving and fast-learning abilities. **⚠️ Critical Gaps:** - Extensive direct experience (7-10 years) in formal project planning, scheduling management, and planning deliverables for large-scale industrial projects. **✅ Strengths:** - Strong engineering and technical system architecture background. - Demonstrated ability to deliver complex technical projects and self-teach advanced concepts. - Solid general industrial and supply chain background. **❌ Missing Required:** Direct experience (7-10 years) in a Project Planning Manager, Project Development Planning Manager, or similar role., Familiarity with planning tools (e.g., MS Project, Primavera, Planisware)., Basic overall knowledge of railway products and good understanding of sub-systems.

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5-mini] Strong alignment on Python, SQL, Git/PR workflows, CI/CD and proven production system ownership; candidate shows high fast-learning potential and domain transferability. Primary shortfalls are explicit Jinja templating experience and hands-on observability tooling, both learnable given background. **✅ Strengths:** - Python automation & debugging for production scripts - SQL-driven data investigation and validation - Systems architecture and hands-on production deployments (JIT inventory, self-hosted AI clusters) **❌ Missing Required:** Jinja templating

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5.1] Strong match on Python, data-heavy systems, and end-to-end ownership, with the main risk being lack of explicit QA-role experience and n8n-specific exposure rather than true capability gaps. **✅ Strengths:** - Python for data, automation, and algorithmic systems - Deep experience with data-heavy, ETL-like workflows and operational analytics - End-to-end ownership of production systems, including reliability and continuous improvement **❌ Missing Required:** Proven experience in QA for software products (explicit QA role or primary responsibility)

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Flash] The candidate is a highly skilled Systems Architect with deep expertise in AI infrastructure and automation, but this role is specifically for a Software Engineer in Test. There's a critical domain mismatch due to the absence of dedicated test automation experience (Playwright, Selenium) and specific SEIT methodologies, leading to a low score despite strong general technical aptitude. **⚠️ Critical Gaps:** - Primary experience in Systems Architecture/DevOps/AI Infrastructure, not dedicated Software Engineer in Test role or quality assurance methodologies. - Lack of direct, hands-on experience with core SEIT tools and practices. **✅ Strengths:** - Strong background in building and automating production systems. - Deep expertise in AI infrastructure and related technologies (LLMs, Docker, AWS). - Proven fast learner with a strong technical career trajectory and analytical skills. **❌ Missing Required:** Hands-on expertise with Playwright and Selenium for UI test automation, Strong programming skills in JavaScript/TypeScript, Proven experience with test management tools like Qase and defect tracking in JIRA, Solid understanding of software testing methodologies (functional, regression, smoke, integration, E2E)

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5-mini] Strong infra+Python/SQL background, proven ability to build production pipelines and self-host LLMs; however lacks hands-on experience with key AWS data services and Spark/streaming which are central to the role. Fast learner and founder experience lowers penalty for gaps. **⚠️ Critical Gaps:** - Apache Spark - AWS Glue / Athena / DMS (managed AWS data services) - Streaming frameworks (Kafka/Kinesis) **✅ Strengths:** - Infrastructure & automation: Docker, Terraform, CI/CD and production system builds - Python + SQL data processing and pipeline automation experience - Experience deploying and optimizing ML/LLM infrastructure; demonstrated fast-learning founder/architect **❌ Missing Required:** Vivência prática com AWS Glue, Experiência com Amazon Athena, Experiência com AWS DMS, Experiência prática com Apache Spark, Monitoramento em Metabase, Experiência sólida em pipelines batch e streaming, Experiência com AWS (ECS, EKS, Lambda) beyond 'basic' knowledge

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Flash] The candidate demonstrates strong technical leadership, operational organization, and a proven ability to architect and automate complex systems. However, there's a significant domain mismatch, as their experience is in AI infrastructure and systems architecture, not direct enterprise IT Operations coordination, service desk management, or specific ITAM/ITIL process execution. **⚠️ Critical Gaps:** - Core domain experience in IT Operations/Service Desk Coordination within an enterprise setting **✅ Strengths:** - Strong technical leadership and operational organization in building and automating systems - Expertise in security fundamentals (SSO, RBAC) and foundational IT infrastructure (Linux, SQL, CI/CD) - Proven fast learner with a track record of quickly mastering complex technologies **❌ Missing Required:** ITSM tools proficiency (e.g., Jira Service Management, Zendesk), Enterprise ITAM processes and ITIL best practices, Direct experience leading IT Operations/Service Desk teams