[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational skills in system architecture, automation, and managing complex workflows, evidenced by their experience with JIT inventory systems, CI/CD pipelines, and self-hosted AI clusters. This aligns well with the core concept of batch/job operations. However, the candidate lacks explicit experience with the specific enterprise tools mentioned: Tivoli Workload Scheduler (TWS), RunMyJobs (RMJ), Jira (for support queues), and ServiceNow (for support queues). Furthermore, direct experience with MainFrame connectivity is also missing. Given the candidate's proven 'fast learner' ability and extensive background in self-teaching and building complex systems, there is a high potential to quickly acquire proficiency in these specific operational tools, though MainFrame connectivity represents a more distinct knowledge gap.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong technical acumen, automation skills (Python, CI/CD), and a proven fast-learner capability, making them highly adaptable to new domains. However, direct, dedicated experience in Quality Assurance methodologies, specific test artifact creation (plans, cases, scripts), defect management tools (Jira, TestRail), and hands-on experience with common test automation tools (Selenium, Cypress, Playwright, Appium) is absent. Their background is more in system architecture and development, rather than a specialized QA role. The score reflects high potential to acquire missing QA-specific skills due to their learning ability, balanced against the lack of direct QA experience.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational software engineering principles, extensive experience in systems architecture, automation, and backend development using Python, along with proficiency in Docker, CI/CD, and basic AWS. Their proven fast-learner trait, evidenced by self-hosting LLMs and algorithmic trading systems, indicates a high capacity to acquire new technologies. However, there are notable direct experience gaps in the specific web full-stack technologies required for this Senior role: Node.js/NestJS for backend and React/CSS for frontend. While the candidate possesses the general 'figure it out' ability and experience in related 'plus' areas, the core web development stack is largely unaddressed in their resume for a senior position.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational skills in automation, CI/CD, Python, SQL, and system architecture, alongside a proven ability for rapid self-teaching, which are highly beneficial for a Test Engineer role. They also have relevant AI experience (a 'plus'). However, the resume lacks explicit experience in a dedicated 'testing role,' specifically in web application and API testing methodologies, and direct familiarity with testing tools like Bruno, Postman, or SoapUI. While the candidate's background suggests a strong capacity to adapt and excel, there's a gap in direct, domain-specific testing experience as a primary responsibility.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong capabilities in system architecture, Python programming, automation, and translating business logic into technical solutions, with direct experience in data-intensive systems like algorithmic trading and self-hosted LLMs. These foundational skills, combined with a proven 'fast learner' ability (evidenced by complex self-hosted projects), suggest a high potential to adapt to the analytical data engineering domain. The primary gaps are explicit hands-on experience with specific Big Data technologies (e.g., Spark, Hadoop), advanced SQL for complex analytical queries, and explicit knowledge of various analytical data modeling techniques and data platform architectures (Data Warehouse, Data Lake, Data Mart). While the candidate understands data flow from an ERP perspective, direct exposure to analytical-specific database design and big data ecosystems is not evident. Their management and leadership experience also aligns well with the 'Manager' aspect of the role, mitigating some of the technical skill gaps through a strong learning aptitude.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational skills in system architecture, automation (Python, Shell Scripting, CI/CD pipelines), and general troubleshooting, which are directly relevant to many responsibilities outlined. Their experience with self-hosted complex systems and rapid self-teaching indicates a high 'fast learner' ability, valuable for adopting new technologies. However, there are significant and specific gaps in enterprise-level virtualization (VMWare suite: vCenter, NSX, vROPS, vSAN, VRLI) and automation (VREALIZE AUTOMATION) platforms explicitly listed as requirements. While the candidate likely has general monitoring experience, the specific tools (Zabbix, Telegraf, Grafana) are not mentioned, nor are other automation tools like Ansible and Salt. The existing CI/CD experience makes the lack of explicit 'Jenkins' less critical.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong core competencies in Python development, system architecture, automation, and technical leadership, evidenced by their experience in building complex, self-sustaining systems like JIT inventory and self-hosted LLMs. Their background includes CI/CD pipelines, API integration, and basic AWS knowledge, providing a robust engineering foundation. Crucially, the candidate's 'fast learner' profile (33 years old, history of rapid self-teaching in areas like algorithmic trading and LLMs) significantly mitigates the impact of specific tool gaps, suggesting a high capacity to quickly adapt and acquire new skills. However, distinct gaps exist in explicit experience with Apache Airflow for data orchestration, Power BI for business intelligence (including DAX and Power Query), and formal knowledge of specific big data architectural patterns such as Data Lake and Data Warehouse. While the candidate's project work strongly implies database interaction, explicit mention of SQL and NoSQL databases as technical skills is also missing, as is direct experience with Agile methodologies like Scrum/Kanban and Data Governance best practices.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates exceptional skills in system architecture, automation, and complex problem-solving, with a proven track record as a rapid learner capable of mastering new technical domains quickly. While the resume does not explicitly detail experience in formal 'software implementation processes' for external clients, 'basic project management,' or 'external user training/support' during an implementation phase, the candidate's extensive background in designing, building, and optimizing sophisticated systems suggests a strong capability to quickly adapt to these responsibilities. The job's technical requirements appear relatively basic, aligning well with the candidate's strategic and problem-solving abilities.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate possesses a strong foundational background in system architecture, automation using Python and Shell scripting, Infrastructure as Code with Terraform, and containerization with Docker. Their experience in designing and implementing complex, self-hosted systems (e.g., LLM clusters, JIT inventory) demonstrates significant 'figure it out' ability and a proven track record as a fast learner. This mitigates some gaps where the underlying domain knowledge is present. However, the role requires deep expertise in specific operational tools and platforms that are not explicitly covered in the resume. Key gaps include advanced AWS capabilities, specific Kubernetes/Rancher orchestration experience, familiarity with Azure DevOps, Jenkins, and Control-M for CI/CD, Windows Server administration, and a comprehensive understanding of monitoring tools like Grafana, Prometheus, and Zabbix. Knowledge of SAST/DAST integration and Java EE application servers (Websphere/JBoss) is also missing. While the candidate's learning agility is a significant asset, these specific tool and domain gaps require a notable ramp-up period.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong proficiency in Python, software development practices, and data analysis, with extensive experience in architecting complex systems and self-hosting/optimizing LLMs, which fulfills the core LLM understanding and preferred LLM experience requirements. The primary gap is direct experience with 'Analytics Semantic Layers' and specific tools like 'Cube Core', 'dbt', or 'LookML'. While the candidate's 'fast learner' trait and background in systems integration suggest a high aptitude for acquiring these skills, they are not explicitly present in the resume.

[ANALYSIS_REPORT] [G2.5 Flash] Candidate possesses extensive experience in systems architecture, integration, and automation, directly aligning with the role's core requirements for connecting systems and validating data flows. Strong background in supply chain and ERP-related improvements, coupled with a proven 'fast learner' ability, makes the lack of explicit experience with specific ERPs (Eclipse, P21, Infor) a minor gap, readily compensable through rapid acquisition of new knowledge.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong alignment with the core responsibilities, particularly in system analysis, technical integration, coding, and basic infrastructure knowledge. Their extensive experience as a Solution Architect and Head of Engineering, coupled with a proven track record as a 'fast learner' in complex domains (LLMs, algorithmic trading), significantly mitigates any minor gaps. While explicit mention of SQL in their listed technical skills is absent, their work with data analysis (Pandas/NumPy) and automated systems strongly suggests familiarity, and their learning agility indicates a quick grasp of specific database technologies. Spanish communication is noted as a desirable, but not strictly required, missing skill.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate possesses a strong engineering background, extensive experience in systems architecture, automation, and data processing (Python, SQL), and a proven track record as a fast learner in complex technical domains (self-hosted LLMs, algorithmic trading). They meet all basic qualifications regarding degree and programming language proficiency. While the job emphasizes large-scale image and video data, and this specific domain isn't explicitly detailed in the resume, the candidate's general data handling capabilities and rapid learning ability suggest a high potential to quickly adapt. The primary mismatch is the candidate's advanced experience level for an intern role, but the prompt specifically asks to identify skill gaps, not experience level discrepancies.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational skills in ERP management, business process analysis, and system integration, aligning well with the core activities of the role. Their experience in optimizing supply chain data flow with ERP and leading production planning analyses directly addresses key job requirements. While specific experience with TOTVS Protheus, Oracle, or SQL Server is not explicitly stated, the candidate's extensive background in building complex, self-hosted systems (including LLM clusters and algorithmic trading platforms) indicates a significant 'fast learner' capacity. This suggests a high aptitude for quickly acquiring proficiency in the specific ERP and database technologies mentioned, mitigating the impact of these skill gaps. The candidate's engineering degree and supply chain analytics background further reinforce their suitability.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational skills in system architecture, automation (Python, Shell Scripting), and IaC (Terraform), coupled with a proven track record as a rapid self-learner and experience building complex self-hosted systems (including AI clusters). This aligns well with the 'figure it out' ability and the general spirit of a DevOps/SRE role requiring continuous learning. Their financial background also provides a strong foundation for FinOps. However, specific experience with Azure and OCI cloud platforms, deeper AWS administration, managed Kubernetes services (AKS/EKS/GKE), Ansible, specific CI/CD tools, and most monitoring and cloud-native storage solutions are currently missing or require further development.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong alignment with the required hard skills. Their extensive experience in self-hosted LLMs, algorithmic trading with Python (Pandas/NumPy), and architecting production-grade automated systems directly addresses the need for advanced applied mathematics (machine learning/operations research) and the ability to write quality production code. They are native in Portuguese and fluent in English, fulfilling the language requirements. While the job mentions working with various data types including documents and images, and the candidate's resume doesn't explicitly detail specific experience with these in an AI modeling context, their proven track record as a 'fast learner' and existing strong foundation in Python and ML principles (LLMs, numerical libraries) indicate a high capability to quickly acquire proficiency in relevant libraries or frameworks for these data types. The primary reason for not achieving a perfect score is the lack of explicit, detailed experience with document and image processing in a data science context. A critical non-skill-based eligibility requirement, current university enrollment in São Paulo, is a separate factor not considered in this hard skill match score.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a very strong profile for this role, demonstrating extensive experience in systems architecture, automation, and building robust, production-grade systems. Their proficiency in Python, SQL, Docker, and AWS basics aligns well with the technical requirements. Furthermore, their proven track record as a 'fast learner' who has self-taught complex technologies (e.g., LLMs, algorithmic trading) is a significant asset for adapting to new frameworks and tools. The role's emphasis on a 'developer who wants to broaden into quality engineering' perfectly matches the candidate's trajectory and capabilities. While some specific tools and technologies are not explicitly listed in the resume, the candidate possesses the foundational knowledge and the 'figure it out' ability to quickly acquire them. The most notable specific technical domain gap is explicit experience with Telemetry Data Pipelines.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate possesses a strong technical background in systems architecture, infrastructure, and development with a proven track record of self-teaching and building complex systems. However, the job description is almost entirely focused on product management, Scrum methodologies, and business analysis functions (stakeholder management, backlog management, product vision, sprint planning, quality acceptance). There is no explicit mention in the resume of experience in these core product-focused domains or agile frameworks. While the candidate's 'figure it out' ability is high, the fundamental domain knowledge for a Product Owner/Scrum Master role is missing.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational skills in Python, SQL, and DevOps (CI/CD), coupled with significant experience in architecting and deploying complex, data-driven automation systems. Their proven 'fast learner' ability, evidenced by self-hosting LLMs and algorithmic trading, suggests a high aptitude for acquiring new technologies. However, there are explicit gaps in specific modern data engineering tools and architectural patterns such as data lakes/lakehouses, streaming platforms (Kafka, Flink), distributed processing (Spark), orchestration (dbt, Airflow), and specific data warehousing concepts (SCD, schema evolution). While the candidate possesses strong underlying principles, direct experience with these specific technologies is not present.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a strong technical foundation and significant experience in building complex, self-sustaining systems, aligning well with the engineering background and analytical capabilities required. His background in Supply Chain and experience with industrial entities (Electrolux, Pronutri) are direct matches for the 'differentiator' of industrial/manufacturing data projects. The candidate's proven track record as a 'Fast Learner' (self-hosting LLMs, algorithmic trading) is a significant asset, suggesting an ability to quickly adapt to new methodologies and tools. He also demonstrates strong command over Python, data concepts, ML/AI applications, and DevOps tools (Docker, Terraform, CI/CD pipelines). However, the resume lacks explicit mention of formal project management methodologies like Scrum or Kanban, and specific tools such as Azure DevOps. While his founder experience implies project leadership and execution, the coordination of multiple, distinct multidisciplinary *data* teams (Data Modeling, Data Engineering, Data Science, Business Analysts) is not explicitly detailed.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate meets the mandatory educational and language requirements. Their Bachelor's in Mechatronics, Robotics, and Automation provides a strong theoretical foundation for Operational Technology (OT). They demonstrate significant experience in systems architecture, software automation (Python), and IT infrastructure (Docker, local AI clusters). However, their professional experience lacks explicit hands-on work with industrial OT systems such as PLCs, SCADA, or specific industrial control networks, which are central to the job's responsibilities. Given their proven 'fast learner' ability and strong general technical aptitude, they are well-positioned to acquire these specific OT skills rapidly for a Junior role.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a strong foundation in system architecture, software development, and infrastructure, with explicit experience in Docker, Terraform, CI/CD pipelines, and API integration. Their proven track record as a 'Fast Learner' and experience with complex self-hosted systems indicate a high capacity to adapt and acquire new knowledge. However, there are distinct gaps in enterprise-level architectural methodologies (12 Factors, SOLID, DDD, TDD, BDD), secure development practices, and specific messaging (Kafka, SQS/SNS, RabbitMQ), caching (Redis), and database (SQL/NoSQL) technologies, which are explicitly sought for a Senior Solution Architect role in a large institution. While some aspects of monitoring and scalability are implied by their experience, explicit strategic experience is not detailed.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a strong profile for the role, particularly in system analysis, requirements mapping, and deep knowledge of industrial/production business segments (Supply Chain, JIT systems). His experience as a founder and independent consultant demonstrates a 'metal-up' approach to system design and implementation, including API integration. While direct experience with full 'ERP system implantation' for external clients is not explicitly stated, his work with ERP improvements and building complex automated systems for his own business suggests a strong foundational understanding and 'figure it out' ability, a trait explicitly highlighted in the fast learner context. The identified gaps (SQL, Power Query) are listed as 'diferenciais' (desirable) rather than strict requirements and are specific tools that a candidate with his technical background and learning aptitude would likely acquire quickly.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a very strong profile for a Solutions Engineer role, demonstrating extensive experience in system architecture, automation, and client-facing technical discussions. Their background in building complex self-hosted systems (e.g., JIT inventory, LLM clusters) highlights a 'figure it out' ability and rapid learning, directly addressing key requirements and differentiators. While strong in general API integration and architectural concepts, specific explicit mentions of advanced cloud expertise, certain security protocols (OAuth, JWT, SSO, LGPD), detailed messaging ecosystems (like Meta API), Vector DBs, and observability are not present. However, these are considered learnable gaps for a candidate with this proven track record and fast-learner context, rather than fundamental domain mismatches.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a strong profile for a senior backend role, demonstrating over 10 years of experience in system architecture, automation, and delivering complex, ROI-focused solutions. Their background as a founder and independent consultant highlights significant autonomy, problem-solving capabilities, and a proven track record of rapidly acquiring new technical skills, such as self-hosting LLMs and algorithmic trading. They possess solid experience with APIs, Docker, CI/CD pipelines, Git, and a strong understanding of performance and system resilience, aligning well with many core requirements. The primary gap is direct experience with Golang; however, their demonstrated 'fast learner' ability and existing backend expertise suggest a high potential for rapid adoption. Other minor gaps exist in specific differentiator technologies like production Kubernetes, DDD, messaging queues, and observability tools, but their foundational infrastructure and development skills are robust.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a strong profile with extensive experience in system architecture, automation, data manipulation (Python/Pandas), and building production-grade solutions. Their background as a founder and freelance engineer highlights significant problem-solving, analytical skills, and a proven capacity for rapid self-learning, directly addressing the 'proactivity and disposition to learn' requirement. Key strengths include understanding of data pipelines (CI/CD), business-focused system design, and the ability to optimize complex processes. However, the primary gaps lie in direct hands-on experience with specific Google Cloud Platform (GCP) services, particularly BigQuery and Looker, which are central to the role. While the candidate has basic AWS knowledge and a demonstrated ability to master new infrastructure, the lack of prior exposure to these specific tools is a noticeable deficiency. The absence of explicit experience with Google Sheets for data manipulation is a minor point, easily bridged by their broader data skills.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong capabilities in AI infrastructure and system automation, with a proven track record of self-hosting LLMs and building complex systems. This aligns well with the 'AI-driven content generation' and 'automate what matters' aspects of the role. However, there are significant gaps in 'must-have' requirements: native Spanish proficiency, explicit experience in writing technical content/SEO blog posts for 2+ years, and understanding of the Mexican market's content consumption habits. While the candidate is a fast learner, these core requirements for an 'AI Content Engineer' focused on the Mexican market are not met.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a capable Systems Architect and rapid learner (self-hosted AI, algo trading) with relevant backend/infrastructure experience. However, the role strictly requires 'Expertise' in Go and AWS/Kubernetes (EKS), whereas the candidate is Python-centric and explicitly lists only 'AWS Basics'. The domain fit is good (Backend/Systems), but the specific tech stack gap is significant for a senior/expert role.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational skills in cloud infrastructure (AWS Basics, Docker, Terraform), system architecture, and development (Python, Shell Scripting, SQL), indicating a solid ability to build and manage robust systems. Their experience with SSO and RBAC reflects an understanding of security principles. However, the resume lacks explicit experience with specific infosec testing tools, offensive cloud security practices, and direct familiarity with frameworks like OWASP Top 10 and MITRE ATT&CK. Furthermore, while the candidate's general systems experience is relevant, specific exposure to Security Information and Event Management (SIEM), Cloud Security Posture Management (CSPM), and Cloud Workload Protection Platform (CWPP) tools, which are critical for the monitoring and triage responsibilities of this role, is not explicitly evident. While the candidate's proven "fast learner" ability mitigates some of these gaps, they remain direct areas for development in a Cloud Security Analyst position.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core data engineering and Python programming requirements, supported by a background in complex system architecture and 'Fast Learner' traits (e.g., algorithmic trading, self-hosted LLMs). The candidate's engineering degree and English fluency align perfectly. The primary gaps are operational/support-specific tools (ServiceNow, ITIL) and specific SQL Server optimization evidence, which are low-barrier to learn given the candidate's technical depth. The candidate may be overqualified for the support-focused aspects of the role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on engineering fundamentals (Python, SQL, APIs, CI/CD) and infrastructure (Docker, Terraform). The candidate's experience building automated JIT engines and algorithmic trading bots demonstrates the required data workflow logic. While he lacks specific 'Modern Data Stack' keywords (dbt, Snowflake), his background in self-hosted AI and Pandas/NumPy indicates a high capacity to bridge these gaps quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is significantly overqualified for a 'Junior' role, possessing advanced proficiency in Python, Automation, APIs, and AI Infrastructure that exceeds requirements. His experience building end-to-end systems (JIT inventory, Trading Bots) proves strong data pipeline capabilities. The only explicit keyword gap ('Data Lakes') is easily bridgeable given his background in 'Local AI Clusters' and 'Supply Chain Analytics'.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core engineering and Python/Pandas automation, which provides a solid foundation for Data Engineering. The candidate's background in Quant and Supply Chain proves ability to handle complex logic and ETL concepts ('JIT inventory', 'risk algorithms'). While specific Big Data tools (Spark, Databricks) are missing, the Job Description explicitly accepts 'interest' in them, and the candidate's 'Fast Learner' profile (self-hosted LLMs) suggests rapid adaptability. The primary gap is the explicit omission of SQL and specific database technologies.

[ANALYSIS_REPORT] [Model: gemini-cli] Exceptional domain alignment: The candidate's specific background in Supply Chain/Logistics (Electrolux, Pronutri) combined with the required technical stack (SQL, APIs, Python) makes them a near-perfect fit for a Supply Chain AI company. The 'Founder' and 'Consultant' experience strongly validates the required ability to bridge technical and non-technical stakeholders ('Solutions Engineer' core competency). The only gaps are specific named ERP ecosystems listed as bonuses, and structured support workflows.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is an overqualified Architect with strong Linux, Automation (Python/Shell), and 'metal-up' hardware skills that cover the Linux/DevOps portion of the role exceptionally well. The 'Fast Learner' trait and self-hosting experience mitigate gaps in specific tools like pfSense or specific switch brands. However, the resume lacks evidence of the Microsoft ecosystem (Windows Server, Active Directory, PowerShell) and enterprise virtualization (VMware/Citrix), which appear to be a significant operational component of this specific support role.

[ANALYSIS_REPORT] [Model: gemini-cli] High potential match despite toolset mismatch. The candidate is an expert in the underlying concepts (Automation, LLMs, API Integrations, Business Logic) using a 'Code-First' stack (Python, Docker) rather than the required 'Low-Code' stack (Microsoft Power Platform). Given his proven track record of building complex, self-healing inventory systems and self-hosting LLMs, he likely understands the *mechanics* of the required tasks better than a typical low-code user and could master the Power Platform tools extremely rapidly. The deduction is strictly for the lack of immediate familiarity with the specific Microsoft ecosystem tools.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate exceeds all core hard skills (English, Process/Systems experience, Tech familiarity). Extensive background in process automation (JIT, Python) and system integration aligns perfectly with Solutions Engineering goals. Only misses the specific 'nice-to-have' platform knowledge (Pipefy) and Spanish language skills.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate has the required Engineering degree and relevant Supply Chain/Industrial background (Electrolux), which aligns with resource planning and critical analysis. However, the role is a heavy Project Management/Planning position (PMB, Shutdowns, Team Leadership) requiring a specific Post-Grad, whereas the candidate's recent profile is heavily specialized in Technical Architecture, AI, and Automation (Builder vs. Manager).

[ANALYSIS_REPORT] [Model: gemini-cli] Strong cultural and domain fit (Risk/Finance/Crypto background aligns with Fraud Prevention). Candidate meets the 'Extreme Ownership' criteria as a Founder/Architect. Strong Python skills satisfy the primary language requirement, though the specific backend stack is Go. 'Fast Learner' trait (Self-hosted LLMs, Algo Trading) mitigates the lack of explicit enterprise-scale ML library keywords (PyTorch/Scikit-learn).

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong 'Fast Learner' match with proven ability to master complex systems (AI clusters, algorithmic trading) and meets the core scripting (Python) and container (Docker) requirements. However, there is a significant gap in the specific enterprise-grade orchestration and Red Hat ecosystem tools (OpenShift, Kubernetes, Ansible) required for this consulting role. His background suggests he can bridge these gaps quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate exceeds the 'Plus' requirements (SQL, Finance background) and possesses deep technical context essential for managing engineering delivery. While the specific keywords 'Jira' and 'Agile' are missing, his experience as a Founder and Solutions Architect proves he can manage scope, delivery, and ambiguity. Tooling gaps are negligible for his demonstrated learning velocity.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate demonstrates strong engineering maturity, architectural thinking, and relevant 'Infrastructure-as-Code' skills (Terraform, Docker, Python) that align well with a Solution Architect profile. However, the role strictly requires 'Mastery' (Domínio) of the AWS ecosystem (Serverless, Networking, Databases), whereas the candidate explicitly lists 'AWS Basics'. His experience with self-hosted clusters and automation suggests a rapid learning curve, but the immediate gap in specific AWS managed services is significant.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong Systems Architect with impressive self-taught infrastructure skills (local AI clusters, automated JIT systems) and automation experience (Python, CI/CD), indicating high aptitude. However, this is a specialist 'Observability' role requiring deep, practical experience with the **Grafana Stack** and **Kubernetes**, neither of which are explicitly listed in the resume. While he understands the concept of 'self-sustaining systems' (which implies monitoring), he lacks the specific hard skills in telemetry pipeline design and the requested cloud providers (Azure/GCP) to be a 'plug-and-play' mid-level specialist.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a highly capable Solution Architect with strong skills in automation, self-hosted infrastructure (Docker, GPU clusters), and backend logic (Python). However, this specific role is a specialized Platform Engineering position strictly requiring 3+ years of deep experience in Apache Kafka and Debezium (CDC). The candidate's resume completely lacks references to Event Streaming, Message Brokers, or Kubernetes Operators. While the candidate has the technical aptitude ('Fast Learner', 'Infrastructure') to learn these, the role requires immediate seniority in a domain that is currently missing from their profile.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate has strong Data Engineering and ETL fundamentals via Python/Pandas and strong business logic (Supply Chain/Finance). However, the role strictly emphasizes 'Alteryx' (a specific low-code ETL tool) which is missing. SQL is also not explicitly listed, though likely implied by his backend/quant background. Highly capable of learning Alteryx rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is an exceptionally strong architectural match, offering robust 'code-first' automation capabilities (Python, Custom APIs, Self-hosted LLMs) that surpass the requested low-code requirements. His 'System Integrator' background aligns perfectly with the process mapping needs. While he lacks specific low-code/SaaS keywords (Zapier, ClickUp), his deep technical expertise in 'metal-up' automation and AI infrastructure indicates he can master these tools rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong technical match for the engineering and architectural demands (Python, Docker, Infrastructure, Systems Design) and demonstrates deep 'metal-level' understanding of LLMs (self-hosting, optimization). The 'Fast Learner' trait suggests he can bridge the gap to specific tools quickly. However, strictly comparing against 'Essential' requirements reveals gaps in the specific AI Application stack: there is no explicit mention of RAG architectures, Vector Databases, or the specific orchestration tool 'n8n', and his 'bot' experience is in trading algorithms, not conversational interfaces (Chatbots).

[ANALYSIS_REPORT] [Model: gemini-cli] Strong conceptual fit. The candidate's 'metal-up' experience with self-hosted AI clusters and complex automated systems demonstrates the requested 'Fast Learner' trait and deep technical capability. While lacking specific enterprise stack keywords (Azure, C#) and high-level wrappers (LangChain), the candidate's foundational knowledge in Python, Infrastructure, and AI internals suggests these gaps can be closed rapidly. The 'Independent Consultant' background aligns well with the client-facing requirements.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong domain alignment (Finance/Crypto) and high aptitude for automation (Self-hosted AI, Trading Bots). However, there is a significant tech stack mismatch: the role requires a TypeScript/PHP ecosystem specialist (Vitest, Cypress, PHPUnit) for dedicated QA, whereas the candidate is a Python/Infrastructure expert. While the candidate fits the 'Automation' aspect, the specific languages and testing frameworks are absent.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong technical alignment with the 'Nice to Have' stack (Cloud, CI/CD, DevOps) and excellent business/process optimization background (JIT, Supply Chain) which fits the 'Process Improvement' requirements. The score is capped because the role heavily emphasizes formal Team Management (recruiting, performance reviews, leading dev teams), while the candidate's recent experience is primarily as a Solopreneur/Freelancer without explicit evidence of managing direct reports.