[ANALYSIS_REPORT] [Model: gemini-cli] The candidate matches the core 'Engineering & Automation' persona perfectly, demonstrating the required 'zero-maintenance' mindset through his self-built JIT systems and algorithmic trading experience. His Python and general infrastructure skills are strong. However, there is a distinct toolset gap: the role requires a specific GCP/Orchestration stack (BigQuery, Airflow, N8n), whereas the candidate's experience is in AWS and custom/local pipelines. Given his 'Fast Learner' profile (self-hosted LLMs), he is highly likely to bridge this gap quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is technically overqualified (Solutions Architect vs. Jr. Analyst). While he lacks specific experience with Mobile App Store workflows (Google Play/iOS) and DRM, his advanced engineering background (Python/Pandas/Automation) far exceeds the 'Advanced Excel' and 'Basic SQL' requirements. He would likely automate the operational tasks rather than just perform them.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate has a strong engineering and data logic background (Python, Pandas, Modeling) and relevant domain knowledge (Supply Chain/Finance). However, there is a significant platform mismatch: the role strictly requires the Microsoft Azure Data stack (Synapse, DataFactory, Power BI/DAX), while the candidate's expertise is in Open Source/AWS/Local Infrastructure. The core concepts (ETL, Data Modeling) are present, but the specific tooling is a gap.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly technical Solutions Architect with a strong background in Systems Engineering and Risk Management (Finance/Supply Chain), satisfying the educational and general 'systems logic' requirements. However, the role is heavily focused on Regulatory Compliance (CSV, GAMP 5), a specific QA framework absent from the resume. While the candidate's 'Fast Learner' trait and deep experience with ERPs/Automation suggest they could adapt quickly, they strictly lack the core required regulatory experience.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is likely overqualified for a standard Monitoring role. His background in self-hosted AI clusters, automated JIT systems, and Python automation proves deep understanding of system stability, metrics, and Linux environments. While specific tools like Zabbix or New Relic are not listed, his experience engineering 'self-sustaining systems' and algorithmic trading bots implies he can master standard monitoring dashboards immediately.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is significantly overqualified for this role. Their experience as a Head of Engineering and Financial Analyst demonstrates high-level project management, stakeholder communication, and organizational skills that far exceed the requirements. While 'Microsoft Office' is not explicitly listed, it is a foundational skill for the candidate's past Financial Analyst and Procurement roles.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is technically overqualified but highly aligned. His 'Production Planner' and 'JIT Inventory' experience is the direct high-level equivalent of 'Queue Management' (optimizing flow/throughput). His self-hosted LLM experience bridges the specific domain gap (Data Labeling for AI). He brings engineering-grade process automation to an operations role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the operational and financial ('FinOps') aspects of the role; the candidate's unique background in Finance and Supply Chain aligns perfectly with the 'Cost Optimization' and 'Governance' differentiators. He possesses core IaC (Terraform) and CI/CD skills. However, strictly required Hard Skills regarding 'DevSecOps' and 'Shift-left' security methodologies are missing, and his cloud experience is listed as 'AWS Basics'/'Local' rather than the requested deep enterprise Azure architecture.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match in high-complexity areas (LLMs, NLP, Python, Automation) and Financial domain. The candidate's experience self-hosting LLMs indicates technical depth exceeding standard 'chatbot curation'. Gaps are primarily specific legacy/enterprise platforms (Dialogflow, Rasa) and specific customer experience metrics (NPS, CSAT), which are easily bridgeable given the candidate's 'Fast Learner' profile and engineering background.

[ANALYSIS_REPORT] [Model: gemini-cli] Critical mismatch in language and domain. The role requires mastery of French (not listed on resume) and deep experience in Property & Casualty Insurance (Assurance de dommages), which the candidate lacks. While the candidate is technically overqualified as an Architect, they lack the specific operational support focus and the essential language requirement for this specific role.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a high-potential match due to deep expertise in Python and underlying AI Infrastructure (Self-hosted LLMs, GPU clusters), which exceeds the 'interest' requirement. His background in complex automation (JIT systems, Algo trading) proves the 'Fast Learner' trait required to master specific frameworks like LangChain or Vector DBs quickly. The only significant hard skill gap is JavaScript/TypeScript, as his experience is heavily Python/Infrastructure focused.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong domain match (E-commerce/FinTech) and solid engineering foundation (Python, Terraform, Automation). However, the role is heavily focused on Data Engineering using specific distributed systems tools (Spark, Kafka) and advanced database administration (tuning), whereas the candidate's data experience is primarily single-node (Pandas/Python).

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core Python automation and architecture skills. The candidate's experience building self-sustaining systems and 'metal-up' architecture aligns well with the 'Data capture stack' and 'Crawler/scraper' responsibilities. The primary gap is the depth of AWS specific services (Lambda, SQS, Fargate), where the candidate lists 'Basic' vs the requested 'Intermediate', but his Terraform and local infrastructure experience suggests a rapid learning curve.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong conceptual match with significant engineering depth. His experience building 'JIT automated inventory systems' and 'algorithmic trading bots' demonstrates high proficiency in the core responsibilities: ETL, data transformation, and automation logic using Python. While he lacks specific experience with the requested enterprise tools (Snowflake, Pentaho), his background in Python/Pandas, Linux, and system architecture suggests he can master these specific tools rapidly. The 'Fast Learner' trait strongly mitigates the tooling gaps.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong architectural and automation foundation with direct matches in Terraform, Docker, and Python. The candidate's background in Finance and 'ROI-focused' systems aligns perfectly with the FinOps and governance requirements. The primary gap is deep, proven commercial experience with AWS (listed as 'Basics' vs 'Priority') and specific orchestration tools like Ansible/Kubernetes. However, the 'metal-up' self-hosting experience and rapid learning track record suggest these cloud-specific nuances can be bridged quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] High potential match. The candidate's 'Metal-up' and automation experience strongly covers the reliability, monitoring, and Linux aspects of the role. However, the position leans towards traditional IT SysAdmin duties (Windows, Network Hardware, Support L2/L3), whereas the candidate is a DevOps/AI Architect. He is overqualified in automation but lacks explicit experience with legacy enterprise stacks (Windows Server, Cisco physical gear).

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly capable Python Systems Architect with strong 'Fast Learner' traits (LLMs, Automation), covering the coding and general architecture requirements well. However, strictly for a 'Data Engineering Instructor' role, he lacks the specific distributed data toolchain experience (Spark, Kafka, Airflow) and explicit Cloud Data Warehouse knowledge (Snowflake/Databricks). He fits better as a Backend/AI Engineer than a traditional Data Engineer.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is an exceptional fit for this visionary, high-agency role. The JD prioritizes 'Smart', 'Fast Learner', 'AI', and 'Complex Challenges' over a specific tech stack, matching the candidate's 'Founder' and 'Self-taught AI' profile perfectly. His background in Algorithmic Trading (Finance) and Automated Systems (Scalability) aligns directly with 'Banking with AI'.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate has strong Backend, API, and System Architecture experience (Python-based) and a highly relevant Financial/Crypto background. While overqualified in general engineering principles for a Junior role, there is a significant syntax gap (Node/Nest/TS vs Python). 'Fast Learner' profile indicates high potential to bridge the language gap quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a high-potential match driven by 'Fast Learner' traits and proven architectural success (Founder/Self-hosted AI). Strong alignment on Python, CI/CD, and System Design. The 'metal-up' and 'Local AI' experience demonstrates deep technical grasp of ML infrastructure, arguably more complex than managed services. The primary gaps are specific enterprise tooling (Kubernetes, Deep Cloud) vs. his self-hosted background, but his trajectory suggests he can bridge this rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is significantly overqualified for this Jr/Mid-level QA role, bringing Senior-level Engineering, Finance, and Automation experience. While he lacks the specific 'Manual QA' and 'Mobile Testing' operational history, his background in building production-grade systems, CI/CD pipelines, and algorithmic trading bots indicates he can master the specific testing frameworks (Cypress, Selenium) and methodologies immediately. His 'Fast Learner' profile (Self-hosted LLMs) mitigates the lack of formal QA keywords.

[ANALYSIS_REPORT] [Model: gemini-cli] The provided Job Description appears to be a generic template or placeholder (containing text like 'Pré-requisito 1', 'Atividade relevante para o cargo 1') rather than a specific listing. As there are no defined hard skills or requirements to analyze, a match cannot be calculated.

[ANALYSIS_REPORT] [Model: gemini-cli] Critical domain mismatch. The candidate is a Senior Backend/Infrastructure specialist (Python, Docker, JIT), while the role requires a Frontend profile with 'wide experience' in Angular, RxJS, and HTML/CSS. Even as a fast learner, the technical foundation is entirely distinct (Python vs. JavaScript ecosystem), requiring a complete career pivot rather than just skill bridging.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is significantly overqualified for a Junior SRE role, demonstrating Senior/Architect level proficiency in automation, IaC (Terraform), and system design. While specific SRE tooling keywords (Observability, Windows Admin) are absent, the 'Fast Learner' evidence (self-hosted LLMs, algorithmic trading) strongly suggests these would be trivial to acquire. Core Linux, CI/CD, and Cloud basics are fully covered.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate meets all mandatory requirements, specifically 'AI/bots' (via LLMs/Trading Bots) and platform implementation (via ERP/Custom Systems). His technical depth (Python, Architecture) far exceeds the 'Analyst' requirement, and his 'Fast Learner' profile (self-hosted AI) suggests he will master Sprinklr (listed as a differentiator) rapidly. The only meaningful gap is the specific Customer Experience domain context.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is significantly overqualified (10+ years Architecture/Founder experience vs. Entry-Level Support). While explicit keywords like 'SQL' and 'Delphi/C#' are missing, his advanced experience with Python/Pandas (Data), CI/CD (Git), and building complex inventory systems proves he exceeds the logic and debugging requirements. He is a 'Fast Learner' who can likely master the specific legacy stack (Delphi/C#) and SQL syntax immediately, though they are technically absent from the text.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core engineering, Python automation, and practical Generative AI experience (self-hosting LLMs). The candidate demonstrates the 'Fast Learner' trait effectively by transitioning domains (Supply Chain -> Crypto -> AI). However, there is a significant gap in the specific cloud ecosystem (Azure AI vs Local/AWS) and orchestration frameworks (LangChain) required by the job.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match. Candidate's experience self-hosting and optimizing LLMs demonstrates the required deep understanding of AI infrastructure, likely covering the 'AI in production' needs better than standard API usage. While specific keywords like 'RAG' and 'Vector Databases' are absent, they are highly adjacent to his 'Local AI Clusters' work. His Python/Backend expertise is solid; lack of 'NestJS' (a nice-to-have) and specific 'PostgreSQL' mention are minor gaps given his rapid learning curve and data-heavy background.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong mismatch in infrastructure generation. Candidate is a Modern DevOps/AI Architect (Docker, Python, Cloud, 'Metal-up'), while the role is a Traditional SysAdmin position (VMware, Enterprise Storage SAN/NAS, NetBackup). Candidate matches perfectly on Linux and Automation (Python/Shell) and has the aptitude ('Fast Learner', 'System Integrator') to master the hardware concepts quickly, but lacks the specific 'solid experience' with legacy enterprise vendors (Dell EMC, NetApp) requested.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is an exceptional match due to his rare combination of technical engineering skills and deep domain expertise in Supply Chain and Logistics (including a MicroMasters from MIT). His experience building JIT inventory systems and managing ERP improvements directly aligns with the problems Canals is solving. As a founder and consultant, he possesses the 'Fast Learner' autonomy and communication skills required to bridge the gap between customers and engineering.

[ANALYSIS_REPORT] [Model: gemini-cli] Major misalignment in core technology and seniority. The role is a Junior C++ position, while the candidate is a Senior Systems/Python Architect. While the candidate has excellent domain alignment (ML Inference, GPU clusters, Linux) and strong auxiliary skills (Python, Git), the specific core hard skill 'Modern C++' is completely absent from the resume. A Mechatronics degree suggests legacy C exposure, but not the C++14/20 required. 'Fast Learner' traits apply, but the language gap is the primary function of the job.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate has a strong engineering background as a 'Systems Integrator' and proven 'Fast Learner' traits (self-hosted LLMs, algorithmic trading), which fits the problem-solving nature of a Solution Engineer. However, there is a significant stack mismatch: the role specifically requires Frontend/Web technologies (TypeScript/JavaScript/HTML/CSS), whereas the candidate is a specialist in Backend/Data/Infrastructure (Python/SQL/Docker). While capable of learning, the core hard skills for web debugging are currently missing.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is an excellent cultural and technical match for a role requiring 'curiosity' and 'deep tech' interest. His background in self-hosting LLMs and 'metal-up' architecture suggests a deeper understanding of AI internals than typical API consumers. He matches the 'Solution Architect' requirement perfectly. The missing skills are primarily specific frameworks (Pulumi, LangChain) where he possesses the direct equivalent or superior foundational knowledge (Terraform, Raw Python/LLM optimization).

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is technically overqualified (Solutions Architect) but fits the 'IT Consulting' domain perfectly. Strong match for 'General Administration', 'PMO', and 'IT Terminology' requirements due to diverse background in Operations and Engineering leadership. Primary gaps are in specific support functions like HR/Recruitment and creative Marketing, though his 'Fast Learner' capability and 'Procurement' (sourcing) background suggest he could adapt to talent sourcing quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong overmatch. He possesses advanced engineering capabilities (Architecture, Python, Docker, AI) that far exceed the position's operational requirements (HTTP basics, Logs, APIs). His specific background in Supply Chain and Finance aligns perfectly with the company's business domain.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is an exceptionally strong match for this innovation-oriented MediaLab role. His background in self-teaching emerging tech (LLMs), designing architectures (Metal-up, Cloud basics), and full-cycle development (Python, Automation) aligns perfectly with the goal of 'prospecting languages' and 'developing solutions'. He exceeds the 'Basic knowledge' requirements for cloud/architecture. His experience managing purchases and suppliers also covers the management responsibilities in the JD.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is significantly overqualified for a 'Junior' position, possessing advanced architectural and engineering experience. He exceeds the 'Differentiator' requirements (Docker, LLMs/Chatbots) by having experience self-hosting models and building clusters rather than just using APIs. Core requirements (Python, APIs) are strongly met. The only specific keyword missing is 'SQL Server', but his background in ERPs, inventory systems, and quantitative analysis (Pandas) guarantees a rapid adaptation to relational databases.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate matches and significantly exceeds the requirements. They possess the specific degree (Engineering), fluent English, and the required programming skills (Python) and statistical knowledge. Their experience as a Founder and Architect demonstrates the requested 'end-to-end' project ability, entrepreneurship, and innovation. There are no identifiable hard skill gaps based on this job description.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for an Innovation/AI pilot role. The candidate's experience self-hosting LLMs and optimizing GPU clusters demonstrates 'Figure it out' capability that exceeds the requirement to merely 'pilot' pre-trained models. Core data stack (Python, Pandas, NumPy) is a direct match. The primary gaps are specific AWS proprietary tools (Athena, Transcribe), but given the 'AWS Basics' and strong architectural background, these are trivial to acquire.

[ANALYSIS_REPORT] [Model: gemini-cli] High match. The candidate is technically overqualified for a 'Jr' role, demonstrating advanced 'Hard Code' automation skills (Python, CI/CD, Self-hosted LLMs) that supersede the 'Low Code' requirements (N8N, Zapier). While the specific low-code tool keywords are missing, his experience building autonomous JIT inventory systems and trading bots proves he commands the core logic required. He can likely master tools like N8N in days. The only genuine gap is the specific Microsoft ecosystem (Power Automate/Teams).

[ANALYSIS_REPORT] [Model: gemini-cli] Strong Technical Match, Domain Mismatch. The candidate possesses the core engineering capability (Python, Automation, AI/LLMs, API Integration) and the 'Architect' mindset required to build the 'backbone'. However, there is a significant gap in the specific domain (RevOps/Marketing vs. Supply Chain/Finance) and the required SaaS stack (HubSpot/Clay). While the candidate can easily learn the tools (Fast Learner trait), the role asks for an architect with 'Solid experience in GTM/Ops', implying immediate strategic knowledge of Sales/Marketing data models which this candidate lacks.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match. The candidate exceeds the 'API consumer' level by having experience self-hosting and optimizing LLMs on local hardware, demonstrating deep technical grasp. His background in algorithmic trading and automated supply chain systems covers the required Data Analysis and Pipeline construction skills well. While specific application-layer keywords like 'Vector Databases' or 'Agent Frameworks' are missing from the resume, his ability to architect 'metal-up' AI solutions suggests these would be trivial for him to pick up. He fits the 'Fast Learner' profile perfectly.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match based on deep technical capability. The candidate's experience 'self-hosting and optimizing LLMs' on local hardware demonstrates a fundamental understanding of GenAI internals that often exceeds simple API integration skills. While the resume focuses on 'metal-up' and AWS/Local infrastructure rather than the specific GCP/Azure suites requested, the 'Fast Learner' profile and robust Python/Automation background suggest these specific cloud tools can be mastered rapidly. The core competency of building autonomous systems is present, even if specific enterprise frameworks (like CrewAI) aren't explicitly listed.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong alignment with SRE mindset (automating toil, resilience, infrastructure-as-code). Candidate's 'Solutions Architect' background and proven ability to build self-sustaining systems (JIT inventory, AI clusters) indicate high potential. Core stack (Python, Docker, Terraform) matches well. Gaps are primarily specific enterprise tools (Kubernetes, Ansible) and deep AWS ecosystem knowledge, which this candidate fits the 'Fast Learner' profile to acquire rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong match due to deep technical expertise in Python, System Architecture, and AI Infrastructure (Self-hosted LLMs). While the resume focuses on 'metal-up' infrastructure and optimization rather than the requested 'Application Layer' stack (LangChain, RAG, APIs), their proven ability to build complex automated systems and self-host models suggests they can master high-level abstraction frameworks (like LangChain) very quickly. The gap is purely in specific tool familiarity, not fundamental capability.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong match for the Backend (Python), AI, and 'Founding' (entrepreneurial) aspects, demonstrating the required 'Fast Learner' trait through self-hosted infrastructure and trading systems. However, a significant gap exists in the 'Full Stack' requirement: the resume lacks any explicit mention of modern Frontend frameworks (React, Vue) or web UI development, which are critical for this role.

[ANALYSIS_REPORT] [Model: gemini-cli] Critical domain mismatch. The candidate is a Backend/Systems Architect with deep expertise in Python and Infrastructure, while the role requires 5+ years of specialized iOS mobile development. As per the evaluation rules, this specific scenario (Mobile vs Backend) is a 'totally missing domain'.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for Python and System Architecture fundamentals. The candidate's experience building complex, self-hosted 'metal-up' systems and JIT engines demonstrates the required 'Senior' depth and 'Fast Learner' trait. While specific web frameworks (Django/Flask) and orchestration tools (Kubernetes) are not explicitly listed, his mastery of Python, APIs, Docker, and Terraform suggests these are negligible gaps he can bridge immediately. The main mismatch is the 'Mentorship' requirement, as his background is heavily skewed towards solo/founder roles rather than leading large corporate teams.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong domain fit for the 'AI/LLM' and 'Architecture' aspects of the role; the candidate's self-hosting and privacy-first AI experience is highly relevant to the product. However, there is a significant technical stack mismatch. The role requires 'Deep fluency' in TypeScript/Web (Angular, Node, GraphQL) for a Tech Lead position, whereas the candidate is heavily specialized in Python, Infrastructure, and Backend/Data Engineering.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong Systems Architect and Engineer with solid automation skills (Python, CI/CD), but there is a significant domain mismatch. The role is specifically for a Senior QA/Test Automation Analyst, requiring established expertise in testing frameworks (Cypress, Appium) and methodologies (Mobile testing, Non-functional testing) which are absent from the resume. While a fast learner, a 'Senior' role typically requires immediate proficiency in the specific toolset.