[ANALYSIS_REPORT] The candidate is an exceptional match, effectively overqualified for the technical requirements. The role requires 'Systems Thinking', 'Reading JSON/YAML', and 'LLM Context', all of which the candidate masters at an engineering level (creating them) rather than just an evaluation level. His specific experience with 'Self-hosted AI clusters' and 'Algorithmic Trading' (logic/risk rules) directly translates to 'Validating agent policy logic'.

[ANALYSIS_REPORT] The candidate is a perfect strategic match for the 'AI Innovation' and 'Operational Efficiency' goals, possessing the exact 'AI Engineering' differentiators (Agents, LLMs, Automation) requested. However, the role is explicitly defined as 'Frontend Heavy (70%)' requiring 'Total Domain' of React/TypeScript and functional C# (.NET), whereas the candidate is a Python/AI specialist with absolutely no listed frontend or C# experience. This is a totally missing domain match despite the strong cultural/goal alignment.

[ANALYSIS_REPORT] The candidate is a strong Python developer with proven automation and complex systems experience (Trading Bots, Self-hosted LLMs). The 'Fast Learner' trait suggests a quick transition from Pandas (Single-node) to PySpark (Distributed). However, the role heavily emphasizes a specific AWS Data Engineering stack (Glue, Athena, PySpark) which are distinct gaps in the resume compared to general 'AWS Basics' and 'Infrastructure'.

[ANALYSIS_REPORT] Critical domain mismatch. The candidate is a senior Backend/Infrastructure professional applying for a Junior Mobile Development role. While the candidate has strong general engineering experience (Python, Cloud, Automation), the resume completely lacks the specific hard skills required for this position: Flutter and Mobile App Development (iOS/Android). This is a 'totally missing domain' scenario.

[ANALYSIS_REPORT] Strong alignment on core Python and AI/LLM requirements; 'Self-hosted AI clusters' experience demonstrates deep understanding of the AI integration needed, likely exceeding standard 'Bedrock' usage. Primary gaps are specific enterprise tools (Kubernetes, AWS Glue) and protocols (MQTT), but the candidate's background in Docker, Terraform, and complex system automation strongly suggests these can be rapidly acquired.

[ANALYSIS_REPORT] The candidate is a strong technical fit for an Implementation Engineer role, possessing the core 'System Integration' and API connectivity skills required. While the resume lacks specific healthcare domain experience (HL7, LIS), the candidate's background in complex, high-stakes domains (Algo Trading, AI Infrastructure) and 'Fast Learner' profile suggests they can rapidly master these standards. The 'SaaS Implementation' requirement is met functionally through freelance and founder roles, though not as a formal corporate title.

[ANALYSIS_REPORT] Significant role mismatch. The candidate is a Systems Architect and Engineer focused on building automation and infrastructure (DevOps/SRE profile), while the role is an L2 Operational Support position in a Security Operations Center (SOC). The candidate lacks the specific, mandatory hands-on experience with the required EDR platforms and core SOC workflows (malware analysis, incident containment), although their Python scripting and general infrastructure knowledge are transferable.

[ANALYSIS_REPORT] Strong foundational match. The candidate's 'metal-up' experience with self-hosted AI clusters demonstrates deep technical competence that likely exceeds the difficulty of using managed services like AWS Bedrock. The 'Fast Learner' trait suggests the specific AWS AI stack will be acquired rapidly. The primary gap is the lack of explicit experience with non-text modalities (Image/Video/Stable Diffusion) and specific proprietary AWS AI tools.

[ANALYSIS_REPORT] Major skill mismatch: Candidate is a senior Backend/Infrastructure Architect (Python/Docker) applying for a Junior/Mid-level Frontend/Integration role strictly requiring TypeScript/JavaScript/HTML. While the candidate is a proven fast learner, the lack of any web frontend experience creates a significant gap for immediate contribution in debugging ad scripts and web integrations.

[ANALYSIS_REPORT] Significant stack mismatch. The candidate is a highly capable Senior Python/Systems Architect, but the role strictly requires 3+ years of Ruby on Rails experience, of which there is no evidence in the resume. While backend concepts (SQL, Architecture) transfer, the lack of the primary language (Ruby) and framework (Rails) for a 'deep experience' contract role is a critical gap.

[ANALYSIS_REPORT] Strong candidate due to exceptional domain fit (Pronutri Foodservice experience) directly matching the 'culinary background' preference. While lacking a formal 'QA Tester' title, his engineering background and ability to build 'production-grade systems' implies necessary testing capability. His Python/Automation skills are a perfect value-add for the company's desire to move from manual to automated testing.

[ANALYSIS_REPORT] Strong technical alignment with the 'Hands-on' and 'LLM' aspects. The candidate's self-taught experience with local LLM clusters and 'metal-up' architecture demonstrates the required deep understanding of model optimization and infrastructure. However, the role is an 'Engineering Manager' requiring 2+ years of leading ML teams and 5+ years in NLP/DL, whereas the candidate's leadership is primarily as a Founder/Operations lead, and 'AWS Basics' falls short of the required deep cloud distributed training expertise.

[ANALYSIS_REPORT] Strong match on technical requirements (SQL, Data Manipulation) and general financial background (Risk Management, Fintech experience). However, the candidate's risk experience is primarily in Algorithmic Trading/Market Risk rather than Credit Lending. Lacks specific experience with 'Credit Policies' and 'Vintage Analysis' (Safra), though his quantitative background suggests he could bridge this gap very quickly.

[ANALYSIS_REPORT] Strong match for the 'Entrepreneurship' and 'Innovation' culture, with direct experience in AI, Data, and Infrastructure. The candidate covers most of the 'value chain' (Back-end, Infra, ML, CI/CD) but lacks explicit Front-end experience which is requested. His background as a Founder aligns perfectly with the 'Business oriented' and 'Consulting' nature of the role.

[ANALYSIS_REPORT] The candidate is significantly overqualified for this internship, bringing senior-level expertise in the core 'AI/LLM' and Python domains that far exceeds the 'basic' requirements. While there is a distinct gap in the specific full-stack web technologies requested (C#/.NET/Frontend), his proven track record as a Solutions Architect and 'Fast Learner' (self-hosting LLMs) indicates he could master these tools rapidly. Ideally suited for a more senior role in the same team, but fully capable of the technical workload.

[ANALYSIS_REPORT] Strong match on the core 'GenAI' and 'Python' requirements. The candidate's background in 'Self-hosted AI Clusters' and 'Algorithmic Trading' demonstrates the required complexity for building Agents. The primary gap is the specific Enterprise Data stack (Databricks/PySpark) required by the role, whereas the candidate uses a 'Local/Metal' stack (Pandas/Docker). However, given the 'Fast Learner' trait and strong SQL/Pandas foundation, the transition to Spark/Databricks is highly feasible.

[ANALYSIS_REPORT] Strong match for the Infrastructure and Python aspects. The candidate's experience self-hosting LLMs demonstrates deeper technical understanding than merely consuming OpenAI APIs, satisfying the core 'GenAI' requirement. Significant overlap in DevOps principles (Terraform/Docker vs. ArgoCD). Main gaps are specific enterprise tooling (Azure vs. AWS, Spark/Databricks vs. Pandas), but the 'Fast Learner' profile suggests these are easily bridgeable.

[ANALYSIS_REPORT] The candidate is an excellent fit for the 'SRE', 'Platform Engineering', and 'Data' categories listed in this general talent pool application. His background in Systems Architecture, Automation (Python), and Infrastructure (Docker, Terraform) directly aligns with the technical domains mentioned. His 'Fast Learner' trait (proven by self-hosted LLMs and algorithmic trading) strongly supports the 'Adaptability' and 'Innovation' requirements emphasized in the description.

[ANALYSIS_REPORT] The candidate has strong foundational skills in Python, SQL, and Architecture, which align with part of the requirements. However, the role is heavily focused on GIS (Geographic Information Systems) and Frontend development (React/Angular/Vue/Node.js), both of which are completely absent from the resume. While the candidate is a fast learner, the lack of specific ArcGIS domain knowledge and the entire frontend stack constitutes a significant gap for this specific position.

[ANALYSIS_REPORT] Strong conceptual match for a Systems Integration role. The candidate's background in building JIT inventory systems and crypto trading bots demonstrates high proficiency in the core logic required for this role (APIs, automation, complex data flow). While specific keywords like 'RabbitMQ' or 'Kafka' are missing, the candidate's architectural experience suggests familiarity with asynchronous processing. Python proficiency satisfies the 'at least one programming language' requirement, though Node.js is listed as a preference.

[ANALYSIS_REPORT] The candidate has strong Strategic and Business alignment (ROI, Market Analysis, Stakeholder Management) derived from Founder and Architect roles. However, the role is a formal Product Owner position requiring specific Agile execution skills (Scrum events, Backlog maintenance) which are not explicitly demonstrated in this engineering-heavy resume.

[ANALYSIS_REPORT] The candidate strongly matches the secondary DevOps/Backend stack (Python, AWS, Terraform, CI/CD) and has proven integration experience. However, this is a Senior role specifically requesting 'Primary Development in Digibee' and deep Salesforce/Vlocity expertise (OmniStudio, BRE, OM), which are entirely absent from the resume. The gap in the primary platform (Digibee) and the core business application (Salesforce) is too wide for a Senior title despite the candidate's fast-learning ability.

[ANALYSIS_REPORT] Strong match on foundational engineering (Python, Cloud, CI/CD, SQL) and infrastructure (Terraform). The candidate's background is more 'Systems/Data/AI' and 'Founder-style' implementation rather than traditional 'Enterprise Software Engineering' (Clean Arch, Microservices patterns). However, the 'Fast Learner' profile (self-hosted AI, algo trading) suggests he can adapt to the specific architectural patterns and observability tools (APM) quickly.

[ANALYSIS_REPORT] The job description outlines requirements for basic knowledge of a programming language and an API/framework. The candidate demonstrates extensive expertise in Python, SQL, and shell scripting, along with significant experience in infrastructure (Docker, Terraform, AWS) and system architecture. Their 10+ years of experience and proven ability to design and implement complex systems (e.g., automated JIT inventory, self-hosted LLMs) far exceed the junior-level expectations implied by the job description's 'Em desenvolvimento' (in development) focus. No specific hard skills mentioned in the job description are missing, and the candidate's 'fast learner' traits reinforce their capability.

[ANALYSIS_REPORT] Significant domain mismatch. The candidate is a technical Solutions Architect and Systems Engineer (Python, Docker, Infra), while the role is a 'Learning Analyst' focused on Corporate Training, L&D strategies, and educational program management. While the candidate possesses the required Project Management and English skills, they lack the core domain expertise in Human Resources and Corporate Education.

[ANALYSIS_REPORT] The candidate is an excellent fit for this general talent pool application. The job description is broad, seeking 'exceptional tech professionals' rather than a specific stack. The candidate's background as a Founder and Solutions Architect, coupled with demonstrated rapid self-learning in AI and infrastructure, perfectly aligns with the requested 'strong problem-solving abilities' and 'passion for continuous learning'. No specific hard skill gaps exist because the role is open to various disciplines.

[ANALYSIS_REPORT] Candidate possesses ideal domain expertise (Procurement/Supply Chain/Finance) that maps directly to the target SAP modules (MM, FI, PM). Technical background (Python, Systems Architect) confirms capacity to master SAP configuration rapidly. The primary mismatch is seniority: Candidate is a Senior Engineer applying for a 'Recent Graduate' Trainee program.

[ANALYSIS_REPORT] Candidate possesses strong operational and financial rigor (Financial Analyst, Procurement) suitable for the budgeting/invoicing aspects, and an Engineering background. While he lacks specific 'Project Insight' and AEC-sector experience, his track record as a Founder/Architect demonstrates high capacity to master new tools (Fast Learner) and manage resources. He is technically overqualified for the administrative components (SharePoint, WordPress) but aligns well with the 'Engineering Operations' scope.

[ANALYSIS_REPORT] Candidate possesses all core competencies (Fluent English, Financial Analysis, Research, Organization/Logistics). As a Founder/Operations Head, they have demonstrated the required 'Proactive' and 'Organized' traits. Main gaps are specific to the role's seniority level (candidate is overqualified) and specific travel coordination tooling.

[ANALYSIS_REPORT] Strong candidate with direct domain alignment (Crypto/Blockchain trading background) and solid engineering fundamentals (Python, Terraform, Docker, Architecture). While missing specific enterprise data engineering tools (Spark, Kafka, Airflow), the candidate's history of building complex, self-hosted systems (AI clusters, JIT engines) and 'Fast Learner' profile suggests they can bridge the tool-specific gaps rapidly. The 5+ years of distributed system/API architecture requirement is met through their diverse project portfolio.

[ANALYSIS_REPORT] Strong match for a Senior/Lead role due to 'Founder' mindset and robust infrastructure skills (Docker, Terraform, Self-hosted LLMs). The candidate's Python/Data/SQL foundation is solid. The primary gap is explicit experience with standard 'Model Training' lifecycles (training/tuning classifiers/regressors) vs. his proven 'Algorithmic/Logic' and 'LLM Inference' experience. However, his ability to self-host AI clusters demonstrates he can handle the hardest parts of ML Ops.

[ANALYSIS_REPORT] Strong match for the Data & Consulting profile, particularly in entrepreneurial spirit, ML/AI innovation, and backend infrastructure/automation (Python, Docker, Terraform). The candidate excels in the 'Bridge' role required to translate technical terms for business. The primary hard skill gap is in Front-end development, which is explicitly listed in the project scope, while the candidate is heavily focused on Backend/Data/Infra.

[ANALYSIS_REPORT] Candidate is a strong 'builder' with relevant self-taught AI/LLM experience and solid Python/Backend foundations. However, this is a Senior role requiring 6+ years of specific production ML engineering. Key gaps exist in enterprise-scale MLOps (AWS SageMaker vs. 'AWS Basics') and big data tooling (Spark/Kafka). While the candidate's 'Figure it out' aptitude is high, the specific seniority and tooling requirements for scalable recommendation systems and K8s orchestration are missing from the profile.

[ANALYSIS_REPORT] The candidate is a strong Systems Architect with proven 'fast learner' capabilities (self-hosted AI, algorithmic trading) and solid backend logic experience. However, this is a 'Specialist' role specifically requesting 'Solid experience' in the Java/Kotlin + Spring ecosystem, whereas the candidate is heavily Python-focused. While architectural concepts transfer, the specific language/framework requirement is a significant gap for a specialist level. Strong fit on culture/autonomy, SQL, and general infrastructure (Docker/Terraform), but lacks specific Kubernetes and Java production experience.

[ANALYSIS_REPORT] The candidate is a strong Systems Architect with solid foundations in Infrastructure-as-Code (Terraform) and CI/CD, which are core requirements. However, the role is heavily specialized in the Azure ecosystem (Azure DevOps suite, Azure Policies), whereas the candidate's primary experience lies in AWS and self-hosted environments. While his high aptitude for self-learning suggests he could bridge this gap, he currently lacks the specific 'Azure DevOps' toolchain expertise required to immediately structure governance for a client.

[ANALYSIS_REPORT] Significant role mismatch. The candidate is a Senior Solutions Architect and Founder applying for an entry-level Administrative Assistant role. While the candidate meets the language requirements (English/Portuguese) and exceeds the technical/AI proficiency ('Fast Learner'), the resume highlights high-level engineering and automation expertise rather than the specific clerical, scheduling, and creative design (Canva) tasks required for this support position.

[ANALYSIS_REPORT] Candidate is significantly overqualified for this Operations Admin role. His experience in 'Supply Chain Analytics' and 'Procurement' (managing 60 branches) far exceeds the requested '1-2 years experience'. While he lacks specific 'Residential/Hospitality' domain keywords, his background in building automated logic and financial analysis (Pandas/Python) guarantees mastery of the required Excel/Reporting tasks. The only true gaps are specific low-level administrative workflows.

[ANALYSIS_REPORT] Candidate is technically overqualified for this internship position. He possesses senior-level expertise in the core requirements (Python, Automation, Data Logic) and exceeds the 'Differential' requirements for LLMs (having self-hosted clusters). His background as a Founder/Architect demonstrates the 'Business Efficiency' mindset the role seeks. The only potential mismatch is administrative (role requires current university enrollment; candidate graduated in 2014).

[ANALYSIS_REPORT] The candidate is a strong Modern Infrastructure Architect (Docker, Terraform, Python) with excellent general 'sizing' and hardware skills. However, the role is a specific Support/Implementation position for a proprietary ERP (Sankhya) requiring a traditional Enterprise stack (Oracle, WildFly, Java troubleshooting). While the candidate's 'Fast Learner' ability covers the OS and general database concepts, the specific requirement for intermediate experience in 'Sankhya W' and heavy reliance on the Java/Oracle ecosystem (WildFly, AWR, Flight Recorder) represents a significant stack mismatch.

[ANALYSIS_REPORT] Candidate is an exceptional fit for the core 'Business-Tech Bridge' requirement, exceeding the analytical depth typically required. His background in Mechatronics and 'Solutions Architecture' covers the system analysis needs thoroughly. While MongoDB is a specific gap, his proficiency in SQL, Python/Pandas, and complex self-hosted data systems indicates he can master it immediately. He likely uses Agile/Kanban principles implicitly in his engineering management roles.

[ANALYSIS_REPORT] Strong match on core automation skills (Python, Bash) and Infrastructure as Code (Terraform). The candidate's 'AWS Basics' is the primary gap for an AWS-specialized role, but their deep experience with self-hosted clusters and general systems engineering suggests they can bridge the gap to specific tools like CloudFormation/CDK quickly. Meets the 'Middle' seniority level easily.

[ANALYSIS_REPORT] The candidate is a strong 'Systems' and 'AI' match who approaches the role from an Engineering perspective (Code) rather than the requested Operations perspective (No-Code). His experience building self-hosted AI clusters and custom JIT inventory systems significantly exceeds the technical complexity of the required 'AI-Enabled Workflows' and 'Automation'. While he lacks the specific SaaS keywords (Airtable, Zapier), his proficiency in Python, APIs, and Data Analysis guarantees he can master these tools immediately. He fits the 'Fast Learner' and 'Independent Problem Solver' profiles perfectly.

[ANALYSIS_REPORT] The candidate has a strong match for the 'AI/ML' and 'Python' components, backed by impressive self-hosted infrastructure and algorithmic experience that aligns with the 'Fast Learner' trait. However, the role is explicitly 'Full Stack' requiring 5+ years of software development and specific Frontend technologies (React) and Backend JavaScript (Node/Express), which are completely absent from the resume. The candidate is a backend/infra specialist, missing the frontend domain required for this position.

[ANALYSIS_REPORT] Major domain mismatch. The candidate is a strong Backend/Systems Architect applying for a Senior iOS role. While they possess transferable general engineering concepts (API integration, CI/CD logic, Python), they lack the entire core mobile technology stack (Swift, UIKit, Objective-C) and the specific 5-8 years of iOS experience required for this senior-level position.

[ANALYSIS_REPORT] Major domain mismatch. The candidate is a senior Backend/Infrastructure Architect with zero evidence of mobile development experience. The role requires deep expertise in native iOS development (Swift, Cocoa Touch), which is a completely distinct domain from the candidate's skills in Python, AI, and Systems Architecture.

[ANALYSIS_REPORT] Major tech stack mismatch. The candidate is a Python/Infrastructure specialist, while the role is strictly for a Java ecosystem specialist (Spring Boot, Hibernate, JPA). Although the candidate has strong general backend concepts (SQL, Architecture, CI/CD, Docker) and is a proven fast learner, the lack of professional Java experience is a critical gap for a Mid-level ('Pleno') role that typically requires immediate framework proficiency.

[ANALYSIS_REPORT] The candidate is a strong Python and Systems Architect with relevant AI context, but the role specifically requires an expert 'AI Tutor' in Kotlin. The candidate's resume shows zero experience with Kotlin, Java, the JVM, or the Android ecosystem, which are the core subject matter requirements for this specific teaching/training role.

[ANALYSIS_REPORT] The candidate is a skilled Systems and Python Engineer, but this role requires deep expertise in Functional Programming (Scala, Haskell, etc.), a paradigm entirely absent from the resume. While the candidate proves they can learn complex systems (AI clusters, trading algorithms), the gap between Python automation and production-grade Typelevel Scala is a fundamental domain mismatch.

[ANALYSIS_REPORT] Candidate is a strong Systems Integrator with proven API and automation experience (Python, JIT systems), which aligns with the core concept of the role. However, this is a niche tool-specific role ('Mulesoft Developer'), and the resume lacks evidence of the specific Mulesoft ecosystem (Anypoint, DataWeave, RAML). While the candidate could likely learn it quickly given their engineering background, the lack of immediate familiarity with the primary toolset is a significant gap.

[ANALYSIS_REPORT] Major domain mismatch. The role specifically requires a Salesforce Developer with deep expertise in its proprietary ecosystem (Apex, Visualforce, Lightning), while the candidate is a Python/AI/Infrastructure Architect. Although the candidate has strong general engineering and API integration skills, they lack the fundamental specific hard skills and languages (Java/C# equivalent for Apex) required for this role.