[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly technical Systems Architect/Engineer with strong capability to fulfill the Systems Analyst role. His experience in 'connecting business goals to technical infrastructure' and building systems 'from concept to execution' covers the core requirements of requirements gathering and prototyping. While he lacks specific legacy keywords like 'Oracle' or formal 'User Stories'/'Scrum' certifications, his background in Python, Automation, and JIT/Supply Chain logic proves he possesses the underlying data modeling and logic skills. He is likely overqualified technically but may need to adapt to the specific bureaucratic/methodological processes (Agile ceremonies, formal documentation) of this role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong potential. The candidate is a high-level fit due to deep experience in Python automation, system architecture ('metal-up'), and 'Fast Learner' traits (self-hosted LLMs). He directly hits the 'LLM' and 'RAG' differentiators. However, he lacks the specific 'Modern Data Stack' tooling required (GCP, dbt, Airflow), though his experience with CI/CD and AWS suggests rapid upskilling capability. Missing explicit SQL mention is a minor gap given his Data/Pandas background.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong fit for the core 'Business Systems Analyst' requirement of bridging technology and business, evidenced by his experience at Atlas Quantum translating financial logic for engineering teams. His technical background (Python, Systems Architecture) exceeds the role's requirements, suggesting he can handle the technical validation and logic mapping with ease. The only significant gaps are the specific NoSQL database (MongoDB) and formal Agile acronyms (DOR/DOD), but his 'Fast Learner' profile and founder experience suggest he can adapt to these specific tools and workflows immediately.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core infrastructure (Terraform, Docker), CI/CD, and practical GenAI application (self-hosting LLMs, local clusters). The candidate demonstrates the high-level 'Figure it out' ability required for complex systems. The primary gap is specific experience with the Microsoft Azure ecosystem (JD demands deep Azure knowledge), but the candidate's background in AWS and bare-metal AI suggests a rapid transition is possible.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match. The candidate's 'metal-up' architectural background and proven 'Fast Learner' ability (self-hosted LLMs, crypto bots) align perfectly with the JD's emphasis on a 'learning-first approach' and 'troubleshooting'. While the candidate lacks specific enterprise Ops tools (DataDog, PagerDuty) and JVM languages (Java/Kotlin), their core engineering stack (Python, Docker, Terraform, Shell) is robust. The JD explicitly states 'Don’t worry if you haven’t used them all before', making the candidate's deep technical aptitude more valuable than specific tool familiarity.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong architectural and leadership profile with proven 'fast learner' capabilities in complex domains (AI/Infrastructure). However, this is a significant mismatch for a 'Technical Lead' role which specifically requires mentoring and code review in a .NET/React stack. The candidate is deeply specialized in Python/Data/Automation. While the 'Backend' and 'Cloud' domains overlap conceptually, the lack of specific stack experience (.NET, React) makes immediate technical leadership in this specific environment difficult.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for Domain (SCM/Warehouse) and Education (Engineering + MITx). The candidate's 'Founder' experience demonstrates practical project execution and ROI focus, covering the core of the role. The primary gap is the lack of explicit, formal corporate Project Management keywords (e.g., PMBOK, Agile, MS Project) which are often standard in large enterprise environments, but the candidate's technical and operational background suggests rapid adaptability.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate significantly exceeds the requirements for this entry-level Software Engineering role. The job description requests foundational knowledge of programming concepts, software design, and the ability to learn under supervision. The candidate's background as a Solutions Architect and Founder, with proven experience building production-grade JIT systems, algorithmic trading bots, and AI infrastructure, demonstrates mastery well beyond the foundational level requested.

[ANALYSIS_REPORT] [Model: gemini-cli] Severe mismatch in role function and industry domain. The candidate is a Senior Technical Architect and Engineer (Builder), whereas the position is a Project Manager in a Digital Marketing Agency (Manager/Coordinator). While the candidate has general business acumen (budgets/scopes) as a Founder, they lack the specific 'Advertising/Marketing Agency' experience explicitly required (3+ years). Their toolset (Python, Docker, Terraform) is engineering-centric, not PM-centric (MS Project, Smartsheet).

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match in Python, Financial/Supply Chain domain, and general infrastructure (Docker/Terraform). The candidate's 'metal-up' and self-hosted AI experience demonstrates high 'figure-it-out' aptitude relevant to data architecture. However, for a Senior/Principal Data Engineer role, the explicit lack of 'Big Data' framework experience (Spark, Modern Cloud DWs, Orchestrators) is a significant gap, though likely bridgeable given the candidate's backend/automation strength.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong architectural and '0-1' founder alignment. The candidate's experience in self-hosting LLMs and building complex automated systems (JIT, Trading) demonstrates the required 'figure it out' capability. Major gaps are specific AI application techniques (RAG, Vector DBs) vs. his Infrastructure focus, and a lack of explicit Frontend/Web Application frameworks, though the latter is mitigated by the 'Bubble' (no-code) option.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is significantly overqualified for this Entry Level Software Engineering role. The job description asks for 'foundational understanding' of programming and design principles, which the candidate vastly exceeds with over 10 years of experience as a Founder, Solutions Architect, and Systems Engineer building production-grade automation and AI infrastructure.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a highly technical Solutions Architect and Engineer, whereas the role is a functional implementation specialist. The candidate lacks the strictly required 'Salesforce' experience (specifically requested 'in the last 2 years'). While the candidate has strong systems integration and analytics skills, their profile is over-engineered (Python, AI, Docker) for a role focused on CRM configuration and user training.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong applied Solutions Architect with relevant AI hardware/infrastructure experience (local clusters, LLMs). However, this is a Research role explicitly requiring a Ph.D./MS and a history of academic publishing, which marks a significant domain gap from the candidate's applied engineering/business background. C++ is a missing hard skill.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong foundational match on core SRE/Engineering skills (Terraform, Python, Automation, CI/CD) and 'FinOps' mindset (Finance/ROI background). The candidate is a proven high-velocity learner (Self-hosted LLMs, Quant Trading), suggesting he can bridge the Cloud provider gap quickly. However, the role is highly specialized in the Azure Data Ecosystem (Databricks, Synapse, Unity Catalog), whereas the candidate's cloud experience is limited to basic AWS and local infrastructure. He fits the 'SRE' profile but lacks the specific 'Azure Data' toolset.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly experienced engineer (10y+) with strong domain alignment (Finance/Logic) and meets all 'Nice to Have' requirements (Python, Cloud, Remote). However, there is a significant stack gap: the role is strictly Full Stack JavaScript (Node/React), while the candidate is a Python/Systems specialist. Given the 'Junior' level and 'Fast Learner' trait (self-hosted LLMs, founder experience), the candidate has the aptitude to pivot quickly, but currently lacks the specific requested web frameworks.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate has strong automation, scripting (Python/Shell), and general infrastructure (Terraform/Docker) foundations suitable for SRE work. However, the role explicitly marks 'OpenStack' as indispensable, which is completely absent from the resume. He also lacks specific mentions of enterprise virtualization, messaging (RabbitMQ), and config management (Ansible), though his 'fast learner' profile suggests he could adapt quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match driven by exceptional Domain + Tech synergy. Although the candidate lacks specific experience with the 'BigQuery/Airflow' stack, his background in Supply Chain (MicroMasters) and JIT Inventory systems makes him highly valuable for a Distribution-focused company. His proven ability to build complex 'metal-up' systems and perform Algorithmic Trading (Python/Pandas) confirms he possesses the required Data Engineering logic (ELT concepts). The missing tools (GCP, Airflow) are adjacent to his existing stack (AWS, Custom Python Automation) and can be learned rapidly given his 'Fast Learner' profile.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a high-potential architectural match with strong Python, automation, and infrastructure skills (Docker, Self-hosted AI). While lacking the specific 'Big Data' toolchain (Spark, Airflow, Snowflake), their proven ability to master complex domains (Algo Trading, LLMs) suggests rapid upskilling. Excellent cultural fit for innovation/challenges, but requires onboarding for the specific data stack.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is significantly overqualified (10+ years experience vs. 'Junior' role) with a strong domain match in Supply Chain and Logistics. While the resume focuses on code-first automation (Python, Pandas) rather than the requested GUI tools (MS Project, Power BI), his 'Fast Learner' profile and engineering background suggest he can master these specific tools immediately. The core competency of project management and financial analysis is fully present.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong domain alignment in Finance and Trading. However, there is a critical technical stack mismatch. The role requires a Microsoft ecosystem specialist (C#/.NET/WPF/SQL Server) and Full Stack UI work (Frontend/WPF), whereas the candidate is a Python/Infrastructure/Automation specialist focused on Backend and DevOps/AI. While a fast learner, the gap spans the entire core development stack.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a senior Python/Infrastructure engineer with strong AI/LLM background, but the job has a specific hard requirement for the 'Blip' platform (chatbots) and prefers .NET (C#), neither of which are listed in the resume. While the candidate's general engineering and AI skills are transferable, the specific tech stack mismatch is significant for this particular role.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is significantly overqualified, possessing advanced data engineering and automation skills (Python/Pandas) that far exceed the position's requirements for Excel-based analysis. While he lacks the specific 'Junior Project Analyst' title and Education/Public Policy domain experience, his background in Supply Chain planning and Financial Analysis demonstrates the required analytical and organizational capabilities.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate possesses the foundational academic background (Mechatronics Engineering) but has pivoted his career entirely toward IT, Cloud, and Financial Software. The role is a Senior Operational Technology (OT) position requiring deep, recent hands-on experience with specific industrial hardware (PLCs, Industrial Networks) which is absent from his professional history. While a fast learner, the gap between his current stack (Python/Docker/AI) and the required stack (Rockwell/Siemens/Profinet) is too wide for a Senior role without significant retraining.

[ANALYSIS_REPORT] [Model: gemini-cli] Significant domain mismatch. The role specifically requires a Network & Infrastructure Architect deeply embedded in the Cisco ecosystem (Hardware, Telephony, Enterprise Security), whereas the candidate is a Software, Automation & Data Architect. While the candidate has strong business acumen (ROI, TCO) and general 'Architect' traits, they lack the fundamental networking engineering background (CCNP level) and specific vendor knowledge required for this pre-sales role.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong Systems Architect with solid DevOps/Infrastructure skills (Docker, CI/CD, AWS basics) and a proven 'Fast Learner' ability. However, this is a Technical Lead role specifically for the Java/Spring ecosystem with React. The absence of the core programming language (Java) and framework (Spring Boot) makes it highly difficult to fulfill the 'mentoring' and 'standard definition' responsibilities immediately, despite general architectural alignment.

[ANALYSIS_REPORT] [Model: gemini-cli] Major Tech Stack Mismatch. The candidate is a Senior Python/Automation Architect, while the role requires a C#/.NET Developer. Although the candidate has strong engineering fundamentals (JIT systems, Algo trading) and is a proven fast learner who could adapt to C#, they currently lack the core Microsoft ecosystem tools (Visual Studio, .NET) and specific language requirements strictly demanded by the job description.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the 'Technical Background' requirement; the candidate's experience in Solution Architecture, DevOps (CI/CD, Docker), and 'Bridging Business & Tech' directly aligns with the role's need for a technical leader. His background in JIT/Supply Chain effectively covers the 'Value Stream Mapping' and process optimization requirements implicitly. The primary score deduction is due to the lack of explicit large-scale corporate Agile framework experience (SAFe, managing multiple distinct software squads) and formal certifications (CSM, PMP) requested.

[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.