[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a high-potential match, particularly for the 'Support to Digital Transformation' and 'AI/Automation' components. His background in Mechatronics and Supply Chain (MITx) fulfills the education and analytical requirements perfectly. He exceeds the 'Differentiators' regarding Generative AI and hyperautomation, effectively bridging the gap between business process and technical implementation. The only gaps are specific commercial modeling tools (easy to learn for an engineer) and formal corporate governance certifications.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong technical match, likely overqualified. Mechatronics Engineering degree validates 'Engineering Drawings' and general 'Hardware' aptitude. Proven experience in Supply Chain/Operations implies Project Management competency, though the specific tool 'MS Project' is missing. Advanced technical skills (Python, Cloud) suggest high adaptability for the required administrative/support tasks.

[ANALYSIS_REPORT] [Model: gemini-cli] Significant role mismatch. The candidate is a skilled Systems Architect and builder, whereas the role requires a dedicated QA Specialist with 5+ years of experience. While the candidate's strong Python, SQL, and Automation skills would allow them to write test scripts, they completely lack the specific QA domain expertise (Mobile/Web testing lifecycles, manual testing rigor) and the required testing frameworks (Selenium, Appium).

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core engineering (Python, Docker, CI/CD) and 'Fast Learner' traits (Self-hosted LLMs, Algotrading). The candidate demonstrates deep 'Infrastructure-level' AI knowledge (Hardware, Optimization, Local Clusters) which aligns well with the 'Open Source Models' requirement. The primary gaps are in the 'Application-level' AI stack (RAG patterns, Vector Stores, Agent Orchestration frameworks) and specific data systems (NoSQL, Kafka), but his architectural background suggests he can rapidly bridge these specific implementation details.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is exceptionally qualified for an 'Entry Level' Data Engineer role, effectively overqualified in engineering maturity. Core requirements (Python, SQL, APIs, DevOps, CI/CD) are strong matches. While specific keywords like 'Spark' and 'Azure' are missing, the candidate's experience with 'Local AI Clusters', 'Algorithmic Trading' (complex data flow), and 'AWS' proves high transferability. 'Fast Learner' trait strongly mitigates specific tool gaps for this seniority level.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is significantly overqualified (Solutions Architect vs. Junior L1 Support) with strong 'fast learner' potential. While expert in Linux/Cloud/Automation (Docker, Python, AI), the resume explicitly lacks the Windows Server ecosystem (Active Directory, Exchange) and enterprise helpdesk tooling (ServiceNow) required for this specific infrastructure support role.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a high-performing modern engineer (Python, AI, Cloud), but the role targets Legacy/Mainframe systems (Cobol, Algol) and Enterprise Java, which are completely absent from the resume. While the candidate has strong business logic and automation skills ('Fast Learner'), the technology stack gap is fundamental.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong conceptual match in Backend Architecture, Microservices (Docker), and Cloud Infrastructure (AWS). The candidate is a proven 'Fast Learner' with complex system experience (AI Clusters, Trading Bots), making the transition from Python to the required NodeJS stack highly feasible. However, the lack of immediate NodeJS and specific protocol experience (GraphQL, SOAP) prevents a higher score for this implementation-heavy role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong alignment with the Forward Deployed Engineer profile, which blends technical consulting with engineering. The candidate's 'Systems Integrator' background and specific experience with 'Self-hosted AI' and 'JIT inventory engines' map directly to 'AI-native solutions' and 'Data pipelines'. The 'Fast Learner' trait (self-taught AI clusters) minimizes the concern over missing specific enterprise orchestration tools like Kubernetes. The main technical gap is a lack of explicit experience with web/API frameworks (FastAPI, Node.js) compared to general automation scripting.

[ANALYSIS_REPORT] [Model: gemini-cli] Major domain mismatch. The candidate is a strong IT/Cloud/Infrastructure Architect with a background in Supply Chain and Finance, whereas the role specifically requires expertise in Automotive Engine Control and Propulsion Calibration. While the candidate's Mechatronics degree provides a theoretical foundation, they lack the specific industry experience and formal Project Management/Agile certifications required for this technical leadership position.

[ANALYSIS_REPORT] [Model: gemini-cli] Major Tech Stack mismatch. Candidate is a highly capable Python/Infrastructure engineer applying for a specialized .NET (C#) role. While general Backend and API architectural concepts are present, the candidate lacks the specific language and framework ecosystem (.NET/C#) required for immediate contribution. High 'Fast Learner' potential allows for pivoting, but the gap is significant.

[ANALYSIS_REPORT] [Model: gemini-cli] Significant domain and stack mismatch. The candidate is a Python-centric Systems Architect and Data Strategist, whereas the role strictly requires a JavaScript/TypeScript specialized Software Engineer in Test (SEIT). While the candidate's infrastructure (Docker) and AI background align with 'Preferred' qualifications, they lack the non-negotiable hard skills in Browser Automation and formal QA methodologies required for this specific role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong architectural and automation background with Python. The candidate is a near-perfect match for the client's specific 'Nice to Have' interest in Agentic AI, LLMs, and RAG, given his experience self-hosting LLMs and building autonomous systems. However, as a core Data Engineer role, he lacks explicit evidence of traditional Big Data frameworks (Spark) and high-performance SQL tuning, relying more on Pandas/Code logic.

[ANALYSIS_REPORT] [Model: gemini-cli] Major domain mismatch. The candidate is a high-level DevOps/Solutions Architect (Python, Docker, AI) focused on building automated systems and engineering. The role is a Corporate IT/SysAdmin position focused on maintaining office infrastructure (Windows, Workstations), User Support (Help Desk), and Vendor Management. While the candidate is overqualified in engineering, they lack the specific 'Essential' operational experience in corporate IT administration (Windows/M365) and user support workflows requested.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is an exceptional match. He holds the required Engineering degree and significantly exceeds the requirements for 'Process Improvement' and 'Report/Tool Development' through his advanced automation skills (Python) and Supply Chain background. His experience as a Founder demonstrating 'Concept to Execution' capability aligns perfectly with the strategic nature of the role. The only potential gap is a lack of explicit 'Formal Project Management Methodologies' (e.g., PMP, Agile frameworks) keywords, though his practical experience is extensive.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a high-potential match with deep Python, Engineering, and AI infrastructure experience (Pandas, NumPy, Local AI) that aligns perfectly with the role's 'Good to have' AI/Data context. While the resume lacks the explicit '3+ years of FastAPI' and specific 'Async' keywords, his background in building production-grade automated systems and API integrations demonstrates the necessary fundamental competence to master the framework rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate strictly fails the 'Eliminatory' requirement for GoLang experience, which is critical for a Tech Lead role expected to mentor others in that language. While the candidate is a strong Python-based Systems Architect with relevant DevOps/Infrastructure context (AWS, CI/CD), the gap in the core application stack (Go, Node, Mongo) is too wide for immediate leadership efficacy.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a high-level Solutions Architect/DevOps Engineer with strong automation and software skills (Python, Docker, AI), whereas the role is a traditional Network/IT Infrastructure Administrator position focusing on connectivity, cabling, and hardware maintenance. While the candidate's engineering background and 'Fast Learner' ability (self-hosted clusters) imply he could easily perform these tasks, the resume lacks specific hard skills in enterprise networking hardware (routers, switches, firewalls) which are the core requirements. He is likely overqualified in development but technically unproven in traditional IT hardware support.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly capable Systems Engineer with a strong track record of self-teaching complex domains (AI, Algo Trading). However, this role is a niche specialist position requiring deep integration between AWS and Salesforce Marketing Cloud (SFMC). While the candidate can likely master the AWS components (Lambda, S3) quickly given their Python/Infrastructure background, the complete lack of SFMC experience and the disparity between 'AWS Basics' and the required '5+ years' deep expertise represents a significant gap.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly technical Systems Architect with strong matches in Infrastructure, Cloud (Basic AWS), and Automation (Python/Shell). While they possess general ERP domain knowledge (Supply Chain/JIT), they explicitly lack the required 'TOTVS' specific administration experience and the 'ITIL Foundation' certification. Their 'Fast Learner' profile and engineering background suggest they could master the specific tool quickly, but they are currently missing these hard requirements for this support-focused role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong foundational match due to deep Python experience (Pandas/NumPy), architectural seniority, and fluent English. The candidate's 'Fast Learner' profile (self-hosted AI, algorithmic trading) suggests he can bridge the gap from local data processing to cloud distributed systems quickly. However, the specific requirement for 'Big Data' tools (PySpark) and managed AWS data services (Glue, Data Lake) represents a distinct technical gap compared to his 'Metal-up' and local infrastructure focus.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong strategic match due to 'Solutions Architect' background and direct experience with LLMs/GenAI (a key differentiator). His 'Fast Learner' trait (self-hosting AI clusters) compensates for specific framework gaps. However, the role requires deep Cloud and Container orchestration expertise (Kubernetes, Advanced Cloud), whereas the candidate admits to 'Basic AWS' and focuses on local hardware/Docker. Business alignment is excellent.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong alignment on Python proficiency, architectural mindset, and cost-optimization focus ('low cost architectures' vs 'ROI-focused'/'Metal-up'). The candidate's background in 'metal-up' and self-hosted infrastructure demonstrates the required 'economic' thinking. The primary gap is the specific GCP ecosystem (BigQuery, DataFlow, etc.), but given the candidate's experience with AWS, Terraform, and self-hosted clusters, this is a transferable skill gap that their 'Fast Learner' trait bridges effectively.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match in core capability (Inventory/Supply Chain logic, Purchasing experience, Data Analysis). The candidate has built complex inventory systems (JIT) and managed IT purchasing, which demonstrates the underlying logic required for IT Asset Management. His Python/Automation skills are a significant value-add for 'optimizing license management'. However, he lacks the specific enterprise formalism (ITIL) and specific legacy tools (SCCM/BigFix) requested. As a verified 'Fast Learner' who self-hosts AI clusters, learning specific licensing rules or CMDB tools will be trivial.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is highly qualified for the automation and data analysis objectives. His expertise in Python and self-hosted AI exceeds the 'no-code' and general 'AI experience' requirements, offering more robust solution capabilities. His Engineering background and Supply Chain experience are strong equivalents for the requested Administration/Economics degree. Main gaps are specific keywords for simplified tools and formal project management frameworks, which are easily bridgeable given his 'Founder' and 'Head of Engineering' experience.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the 'Software Engineer willing to transition' criteria. Candidate has deep architectural and Linux experience (Docker, AI Clusters). Critical gaps are the specific tool (JMeter) and preferred language (Java/JVM), but the candidate's track record in complex domains (AI, Algo Trading) proves rapid upskilling capability.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a high-potential Architect/Engineer with strong skills in modern automation, Python, and strategic systems thinking. However, the Job Description is heavily weighted towards specific Enterprise Legacy stacks: SAP (FICA, FI/AA), Salesforce, and Mulesoft. While the candidate matches the 'Automation' and 'Architecture' aspects conceptually, the lack of specific SAP ecosystem experience—often a non-negotiable hard skill in such roles—creates a significant gap that 'fast learning' cannot immediately bridge for a senior/architect level role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong foundation in Infrastructure-as-Code (Terraform) and Automation (Python). The candidate's background in building self-hosted AI clusters and complex JIT systems proves the 'Fast Learner' trait, mitigating the lack of specific tool experience. Primary gaps are explicit Kubernetes orchestration and enterprise-grade observability tools, though the underlying concepts (containers, monitoring) are present.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong Systems Architect and 'Fast Learner' with solid experience in Infrastructure, Python, and Automation. However, this role is specifically for a Technical Lead in the JavaScript ecosystem (React/Node.js). While the candidate has the seniority and architectural mindset (Docker, CI/CD, System Design), the total absence of Frontend (React) and the specific Backend stack (Node.js) creates a significant gap for a leadership position expected to mentor on those technologies.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a high-aptitude engineer with strong Python, automation, and architectural skills. While lacking specific experience with the required Big Data stack (Spark/Databricks) and Azure ecosystem, his background in Pandas, complex system design (JIT/Trading), and self-hosted AI infrastructure demonstrates high transferability. He fits the 'Fast Learner' profile perfectly, capable of bridging the gap from local/single-node processing to distributed cloud pipelines quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong match for the 'Business' and 'GenAI' aspects of the role, bringing Founder-level commercial awareness and practical LLM deployment experience. The 'Fast Learner' trait is evident in his transition from Supply Chain to AI/Tech. Key gaps are in enterprise-scale orchestration (Kubernetes) and deep cloud governance, where he lists 'Basics' versus the 'Solid experience' required for an Architect role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match in core domain (Supply Chain + Engineering background). The candidate's 'Head of Operations' and 'Independent Consultant' experience aligns well with Project Management and independent delivery requirements. The candidate is technically over-qualified (Python/AI), but the transition to a Consulting Project Manager role is supported by their Supply Chain MicroMasters and business ownership experience. Main gaps are functional pivots towards specific Sales and Recruitment activities requested.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong Engineer/Architect with solid Python and Financial domain experience. However, this is a Senior Specialist role requiring deep specific knowledge in GCP (BigQuery) and Enterprise ETL tools (Informatica), which are not present in the resume. While the candidate is a proven fast learner (Self-hosted LLMs) and knows AWS basics/Python data stacks, the gap in the primary required cloud platform and legacy ETL tools is significant for a Senior position.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate possesses strong foundational 'metal-up' AI expertise (hosting local LLMs, Python, AWS), which exceeds the complexity of the required SaaS-based Agent configuration (Bedrock/Copilot). proven 'Fast Learner' ability with complex self-built JIT/ERP systems mitigates the lack of specific Microsoft enterprise tooling experience. High aptitude to adapt, though specific corporate integration points are missing.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong Systems & AI Engineer (Builder) applying for a Data Science/Analytics role (Modeler). While he matches the Python and English requirements and demonstrates high technical aptitude (LLMs, Quant Trading), he explicitly lacks the core Data Analysis stack requested (SQL, R, SAS, Power BI) and specific classic ML modeling keywords.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong technical match for the 'Engineer' aspect of 'GTM Engineer', possessing superior skills in Automation, SQL, APIs, and System Architecture (Python, Docker, API integration) compared to the typical analyst profile. His 'Founder' experience and financial background suggest a strong grasp of business metrics (ROI, efficiency), bridging the gap to Growth metrics (CAC/LTV). However, there is a distinct domain gap: he lacks specific experience with the standard Marketing Technology stack (GA4, GTM, SEO, Email Infrastructure). Given his 'Fast Learner' profile and history of building complex self-hosted systems, he can likely master these tools rapidly, but he does not currently list them.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a highly technical Solutions Architect/Founder, whereas the role is a client-facing, process-oriented Delivery Manager. While his background as a Founder and in Supply Chain proves he can manage complex projects and operations (adjacent to Delivery Management), he lacks the specific 3+ years of formal 'Delivery Manager' experience in a software agency context. He fits the 'Technical Lead' profile better than 'Service Delivery'.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong candidate for a Junior/Pleno role. The 'Founder' and 'Architect' background proves high capability in Linux, Automation (Python/Shell), and System Design, exceeding the core 'Basic' requirements. 'Terraform' is a direct high-value match. While explicit keywords for specific observability tools (Grafana/Prometheus) and orchestrators (Kubernetes/EKS) are missing, the experience with 'Local AI Clusters' and 'Self-hosted Systems' suggests strong transferability. The candidate is likely overqualified in problem-solving but fits the technical tooling profile for a Pleno role.

[ANALYSIS_REPORT] [Model: gemini-cli] O candidato possui forte alinhamento com os requisitos de análise crítica, otimização de processos e gestão de recursos (Supply Chain/Founder). A experiência prática como Fundador e Planejador cobre as competências de gestão exigidas. A principal lacuna é a exigência formal de tempo de experiência específico em 'Gerenciamento de Projetos' e a Pós-Graduação na área, embora a formação no MITx (Supply Chain) seja altamente relevante e compense parcialmente.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is vastly overqualified (Solutions Architect applying for Jr Support), possessing advanced Engineering/Automation skills that supersede the role's 'Process Mapping' and 'Linux' requirements. Excellent match for the unique 'Purchasing/Inventory' requirement due to specific Supply Chain background. Primary gaps are in specific legacy Office IT administration (Windows Server/AD, Telephony, Cabling) which are distinct from his modern DevOps/Cloud stack, though his Mechatronics background implies easy adaptation to hardware tasks.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core logic (Python, Data Integration, Automation) and Domain (Logistics/Finance data flows). The candidate's 'metal-up' and Terraform experience mitigates the 'Basic' vs 'Solid' AWS gap, as infrastructure concepts transfer well. Primary gaps are specific tool stacks (Databricks, Airflow), but the candidate's proven ability to build complex, self-hosted JIT/AI systems suggests high adaptability to learn these orchestrators and platforms quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] While the candidate is a proven high-velocity learner (self-hosted AI, algo trading) and architect, there is a significant mismatch in the specific 'High Scale' and 'Distributed Systems' requirements. The role demands 10+ years of digital product development (candidate has ~6 years dev focus) and deep expertise in serving millions of users, whereas the candidate's successes are in self-contained/self-hosted systems. The explicit 'We will teach you Clojure' boosts the score slightly, but the infrastructure gap (Kubernetes/Kafka at scale) is substantial.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong conceptual fit. Candidate has effectively performed the exact function of the role (automating operations/inventory) using Python and AI in a self-directed capacity. High 'Fast Learner' bonus applies for self-hosting LLMs and algorithmic trading. Primary gaps are specific tool-stack related (Retool/Salesforce) and the lack of explicit JavaScript experience, which is critical for Retool, but easily bridgeable given Python proficiency.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate exhibits strong architectural thinking ('metal-up', self-hosted LLMs) and high agency ('Founder', 'Algo trading'), fitting the 'Senior/Lead' profile well. However, the role leans heavily towards an Enterprise Web/SaaS stack (Java/JVM, Node, Angular) while the candidate is a Python/Infrastructure specialist. His 'Fast Learner' trait mitigates the risk, but the specific language ecosystem gap is significant for a hands-on architectural role.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate has strong alignment with the technical context (AI, DevOps, Analytics, ERP) and financial acumen required for the projects. However, the role is heavily focused on Service Delivery Management, Contract Management, and the specific Insurance domain (Seguros/SUSEP), which are not present in the resume. The candidate's 'Builder/Architect' profile contrasts with the 'Manager/Controller' (PMBOK/MS Project) requirements of the role, though his founder experience bridges some business management gaps.

[ANALYSIS_REPORT] [Model: gemini-cli] Critical domain mismatch. The candidate is a Senior Software/Systems Engineer and Solutions Architect focused on IT, AI, and Supply Chain automation. The role is strictly for a Civil Architect/Coordinator specializing in construction permits, real estate legal approvals, and civil engineering software (Revit/AutoCAD) within the Rio de Janeiro regulatory framework.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is an exceptionally strong match due to direct Supply Chain domain expertise and proven experience integrating APIs and ERP logic. While specific ERPs (Eclipse, P21) are not listed, the candidate's background in ERP modeling and 'Fast Learner' ability (self-hosted LLMs, algorithmic trading) ensures rapid adaptation. Native Portuguese is a bonus asset.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong foundational match due to Mechatronics Engineering degree (which covers HW/Schematics) and 'Systems' mindset. High potential to bridge gaps given 'Fast Learner' track record (Crypto -> AI). Deductions for lack of specific transit/vehicle protocols (J1939/J1708) and recent focus on Cloud/Software rather than electrical hardware troubleshooting.

[ANALYSIS_REPORT] [Model: gemini-cli] Significant mismatch in role focus. The candidate is a technical 'Builder' (Solutions Architect, Python Dev, Automation) while the role is a formal 'Manager/Controller' (Project Management, Reporting, Process Modeling). While the candidate possesses strong process optimization context (Supply Chain background), they lack the specific formal methodologies (BPM, Six Sigma), the specific tooling (Bizagi), and the required 7 years of formal Project Management experience.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a high-aptitude Systems Architect with strong Python, Automation, and DevOps (Docker/Terraform) foundations essential for the role. While strictly missing specific Modern Data Stack tools (DBT, Airflow) and explicit 'Data Engineering' titles, his experience with self-hosted inventory engines and algorithmic trading (Pandas/NumPy) demonstrates the required data manipulation and system scaling capabilities. A 'Fast Learner' profile who can likely bridge the tool gap rapidly.