[ANALYSIS_REPORT] The candidate is a highly capable technical architect with strong SQL and logic skills, demonstrating a 'Fast Learner' profile suitable for mastering new tools. However, the role is a deep expert position strictly requiring 7+ years of legacy Microsoft Access development, a technology completely absent from the candidate's modern Cloud/AI/Python stack.

[ANALYSIS_REPORT] Complete domain mismatch. The position requires a Civil Engineer specializing in infrastructure construction (roads, drainage, earthworks) with specific CAD/BIM tools. The candidate is a Software/Systems Architect with a background in Mechatronics and Supply Chain. The hard skills (Civil 3D, DNIT standards, Civil Engineering degree) are non-transferable from the candidate's tech stack.

[ANALYSIS_REPORT] Strong match for the primary Python and Infrastructure (Terraform, Docker) requirements. The candidate's background in automation and 'metal-up' architecture aligns well with the engineering needs. However, the role requires maintenance of legacy/alternative stacks (Java, .NetCore) which are absent from the resume. His 'Fast Learner' profile (self-hosted LLMs, algorithmic trading) mitigates the AWS specific gaps (EKS, Glue), suggesting he can bridge the tool-specific deltas quickly.

[ANALYSIS_REPORT] Exceptional match for the specific 'Open Weights' and 'Self-hosted LLM' requirements, which aligns perfectly with the candidate's recent projects. The candidate's 'Founder' experience satisfies the 'Product Manager' responsibilities. The only explicit keyword gaps are specific Python API web frameworks (e.g., FastAPI, Flask), but the candidate's strong Python automation and Systems Architecture background suggests this is a trivial gap to bridge.

[ANALYSIS_REPORT] The candidate has a strong background in Systems Integration (SSO, APIs), Risk Management (Finance/Supply Chain), and English fluency, which aligns well with the consulting profile. However, the role strictly requires 2+ years of hands-on experience with the **OneTrust** platform and specific Privacy Regulations (GDPR/LGPD), which are major hard skill gaps despite the candidate's 'Fast Learner' ability.

[ANALYSIS_REPORT] Candidate is a strong Infrastructure/Automation engineer but has significant gaps in the specific stack requested. The role requires 'Mobile CI/CD' (App Center, Bitrise) and 'NodeJS', whereas the candidate is heavily focused on Python and general backend/AI infrastructure. While Terraform and AWS align, the specific tooling (Jenkins, Dynatrace) and the Mobile DevOps focus are missing.

[ANALYSIS_REPORT] Major Tech Stack Mismatch. The candidate is a strong Python/AI Automator, whereas the role strictly requires a Microsoft stack specialist (Dynamics 365, X++, C#, .NET). While the candidate has relevant domain knowledge (ERP usage, Supply Chain, Finance), they lack the specific 5+ years of hard technical skills in the required ecosystem.

[ANALYSIS_REPORT] Strong match due to the role's heavy emphasis on 'AI-driven automation' and 'intelligent systems,' which aligns perfectly with the candidate's expertise in self-hosted LLMs and Python automation. While the candidate lacks specific QA tooling experience (Playwright), their engineering background and 'Fast Learner' trait make this a trivial gap to close, especially given the strategic architectural nature of the role.

[ANALYSIS_REPORT] The candidate is a highly capable Systems Engineer and Architect with a strong background in Linux, Automation, and AI infrastructure. However, the role is strictly focused on the Microsoft Enterprise ecosystem (M365, Intune, Windows Server), which represents a distinct stack from the candidate's Python/Linux/AWS experience. While the candidate matches on Identity concepts (SSO/SAML) and general systems engineering principles (documentation, infrastructure), the lack of specific hands-on experience with the Microsoft administration suite is a significant gap, though his fast-learning ability partially mitigates this risk.

[ANALYSIS_REPORT] Strong match for Python and core LLM requirements, with self-hosting experience demonstrating deep technical aptitude ('Fast Learner'). The candidate's 'AWS Basics' and local cluster experience are adjacent to the specific AWS AI services required, making them easily learnable. The primary gap is explicit experience with non-text modalities (Audio/Video), as the resume focuses heavily on Text (LLMs) and Data structures.

[ANALYSIS_REPORT] Strong foundational match. The candidate demonstrates Senior-level capability in Data Modeling and Automation via complex custom systems (Algorithmic Trading, JIT Inventory). His transition to self-hosted AI infrastructure confirms the 'Fast Learner' trait required by the JD. However, strictly as a 'Senior Data Engineer', he lacks explicit experience with standard enterprise Big Data orchestration and streaming tools (Airflow, Kafka, Spark/EMR), having relied on custom Python/SQL solutions instead. He fits the 'Avid Learner' profile perfectly but would need to ramp up on the specific stack.

[ANALYSIS_REPORT] The candidate exhibits a strong 'SRE mindset' through automation (Python), IaC (Terraform), and proven ability to architect resilient self-hosted systems ('Fast Learner'). However, for a 'Staff' level role, the explicit lack of production Kubernetes experience, deep enterprise Cloud knowledge (listing only 'AWS Basics'), and standard Observability tooling (Prometheus/Grafana) are significant gaps. He has the foundational skills to adapt, but lacks the specific toolchain mastery required for immediate Staff-level leadership.

[ANALYSIS_REPORT] Major domain mismatch. The role is a Senior/Lead Frontend Engineering position requiring deep expertise in React and the browser ecosystem. The candidate is a Backend/Infrastructure Architect with no mention of Javascript, Frontend, or UI development in their history. While they possess the 'nice-to-have' Infrastructure skills (Terraform, CI/CD), they lack the mandatory core technical stack.

[ANALYSIS_REPORT] Fundamental domain mismatch. The candidate is a Backend/AI Architect with strong Python and Infrastructure skills, while the role strictly requires a Mid-level Salesforce Specialist. The candidate lacks all primary hard skills (Apex, LWC, Salesforce Ecosystem) which are proprietary and distinct from general backend engineering. While a fast learner, the requirement for 2-3 years of specific platform experience makes this a 'non-starter' match.

[ANALYSIS_REPORT] Candidate fits the 'Business Acumen' and 'Project Management' criteria well through their Founder and Analyst roles. However, the role explicitly requires 2+ years of specific 'CRM consulting' or Product Management experience. The candidate's primary domain is AI Infrastructure, Supply Chain, and ERP, which is adjacent but distinct from the sales/marketing focus of CRM. Strong potential to pivot due to 'Fast Learner' traits, but lacks the core hard skill of CRM expertise.

[ANALYSIS_REPORT] While the candidate's Mechatronics degree provides a relevant theoretical foundation for embedded systems, there is a significant gap in recent, practical application of Automotive protocols (CAN, UDS) and the specific required tech stack (C#/.NET). The candidate is a strong generalist and automation expert, but this role requires specialized, low-level diagnostics and firmware analysis experience that is not present in the professional history.

[ANALYSIS_REPORT] Candidate possesses strong foundational infrastructure knowledge ('Metal-up', Local AI Clusters, Docker) and demonstrated rapid learning ability suitable for Ops roles. However, the role strictly requires specific enterprise virtualization suites (VMware, XenServer, Huawei DCS) which are not present in the resume. The candidate is a strong fit for Modern DevOps/AI but a partial match for legacy Hypervisor Administration.

[ANALYSIS_REPORT] The candidate is a technical 'builder' (Architect/Founder) rather than a process-oriented Project Manager. While the Engineering degree and AI expertise are strong matches, the resume completely lacks the strictly required Agile certifications (CSM) and specific Project Management tooling experience (Jira/Atlassian) demanded by the role.

[ANALYSIS_REPORT] The candidate is a strong Python engineer and Systems Architect with proven 'figure it out' capability (LLMs, Trading Bots), making the transition to Web Scraping highly feasible. However, the resume lacks explicit experience with the specific scraping domain (Scrapy, anti-bot mechanisms, headless browsers) and JavaScript, which are core requirements for this specific support role.

[ANALYSIS_REPORT] The candidate has strong overlaps in Data/Backend logic (Python, SQL, Pipeline Orchestration) and domain relevance (Finance/Supply Chain). However, the role is explicitly 'Full-stack' requiring Node.js and Angular, neither of which are present in the resume. While the candidate's 'Fast Learner' profile suggests they could adapt, the lack of any web frontend framework experience is a significant gap for this specific role.

[ANALYSIS_REPORT] Strong match for the specific AI/LLM requirements, backed by a proven track record of self-hosting and optimizing models (Fast Learner trait). However, a significant gap exists in core web application frameworks; the resume emphasizes Python for automation, data, and infrastructure rather than the requested 'Django' web development or 'Multi-tenant SaaS' architecture experience.

[ANALYSIS_REPORT] The candidate is a highly capable Systems Architect and automation expert with a strong 'Fast Learner' profile (self-hosted AI, algo trading). However, this is a Senior Security Operations (SOC) role. The resume lacks specific experience in core SOC domains: Incident Response, Threat Intelligence, and SIEM tools. While the candidate has strong automation (Python) and general infrastructure skills, the gap between 'AWS Basics' and the required 'Strong experience' for a Senior Cloud Security role is significant. He would likely excel in DevSecOps but lacks the specialized compliance and threat-hunting background for this specific position.

[ANALYSIS_REPORT] The candidate is a highly experienced Senior Engineer/Architect with strong Backend and Data logic skills (Python, SQL), which aligns with the 'Backend' domain. However, the role explicitly requires '1+ years of experience in Java', which is completely absent from the resume. While his OOP knowledge (via Python) and general engineering maturity are strong positives that would allow him to learn quickly (Fast Learner), he currently lacks the primary hard skill required for this specific language-focused role.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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