[ANALYSIS_REPORT] Strong match for the Python backend and Cloud infrastructure requirements. The candidate's background in automation, API integration, and self-hosting aligns well with the 'hacker spirit' and technical depth required. However, the role is explicitly 'Fullstack' with a requirement for React, which is completely absent from the resume. While the candidate can likely adapt quickly given their 'Systems Architect' profile, the lack of explicit web framework experience (Django/Flask/FastAPI) and frontend technologies constitutes a significant gap for a senior role.

[ANALYSIS_REPORT] Strong foundation in Cloud Infrastructure (Terraform, Docker, AWS) and Backend data/automation (SQL, Python), matching the 'Infrastructure as Code' and 'Database-Savvy' requirements well. However, this is a Principal Full-Stack role requiring high proficiency in Client-side development (React/Vue) and structured Software Engineering practices (MVC, TDD), which are completely absent from the resume. The candidate fits a DevOps/Systems Architect profile better than a Principal Product Engineer.

[ANALYSIS_REPORT] Strong match on core Python and the specific AI/LLM integration requirement, validated by hands-on experience with local AI clusters. The candidate's 'fast learner' profile mitigates gaps in specific AWS managed services (Glue, Step Functions) and Kubernetes, as they already possess foundational skills in Docker, Terraform, and general cloud architecture.

[ANALYSIS_REPORT] The candidate possesses a strong technical foundation in infrastructure (Terraform, Docker), AWS basics, and identity management (SSO, RBAC), which are transferable to this role. Given their background in building complex self-hosted systems ('Fast Learner'), they likely have the aptitude to master the specific security tools quickly. However, the resume lacks direct experience with the specific vendor stack requested (GCP, Wiz, Palo Alto) and the required regulatory compliance frameworks (HIPAA/SOC2), which are central to this support-focused position.

[ANALYSIS_REPORT] Severe domain mismatch. The candidate is a Software/Systems Architect and AI Engineer, while the role is strictly for a Chemical R&D Coordinator focusing on physical cosmetic formulas. The candidate lacks the fundamental background in chemistry, materials engineering, and product formulation required for this position, despite having transferable general skills in project management and supply chain.

[ANALYSIS_REPORT] Major domain and stack mismatch. The candidate is a Systems Architect/Builder using Python, while the role specifically demands a Senior SDET with 10+ years of dedicated QA/Testing experience and C#. While the candidate's automation and API integration skills are transferable, the lack of the specific required language (C#), the Azure cloud environment, and the decade-long testing pedigree makes this a low-probability match.

[ANALYSIS_REPORT] Strong match due to deep Python automation experience, Infrastructure (Docker/CI/CD) skills, and significant Finance/Forex domain knowledge (CryptoCall, Atlas Quantum). The candidate creates 'self-sustaining' production systems, which implies a quality mindset. Main gaps are formal QA-specific artifacts (Test Plans, STLC) and specific mobile testing tooling, but these are easily bridgeable for a Solutions Architect.

[ANALYSIS_REPORT] The candidate has a strong foundation in Python, Systems Architecture, and DevOps (Terraform), matching the core engineering requirements. His experience as a founder and self-hosting LLMs proves the 'Fast Learner' trait, valuable for adapting to the specific Data Mesh architecture. However, there is a significant gap in specific 'Big Data' tools (Spark, Hadoop) and deep AWS Data services (Glue, EMR), where he currently only lists 'AWS Basics' versus the requested 'Mastery'.

[ANALYSIS_REPORT] The candidate is significantly overqualified for this Junior position but matches 100% of the technical requirements. He possesses specific ERP experience (Pronutri), deep system understanding (Solutions Architect), and explicit knowledge of permission configurations (RBAC). His completed engineering degree supersedes the 'currently studying' requirement.

[ANALYSIS_REPORT] The resume content was empty; assessment is based solely on the provided 'Fast Learner' context (Self-hosted LLMs, Algorithmic Trading). While the candidate shows strong aptitude for AI technologies (LLMs) and complex logic (Trading), there is zero evidence of the specific required enterprise stack (Java/.NET/Node.js, N8N, Cloud). The candidate has the 'figure it out' factor for RAG/Agents, but currently lacks the mandatory hard skills defined in the Job Description.

[ANALYSIS_REPORT] Significant domain mismatch. The candidate is a highly technical Solutions Architect/Backend Engineer, whereas the role is strictly for a Mobile QA Analyst. While the candidate has 'Automation' and 'CI/CD' skills, they lack the specific professional background in Mobile Application testing, formal QA strategies, and the required testing tools.

[ANALYSIS_REPORT] The provided resume content is empty. While the candidate is flagged as a 'Fast Learner' with self-hosting/trading experience (implying potential technical aptitude), no specific hard skills could be verified against the requirements (Golang, AWS, Kubernetes, etc.).

[ANALYSIS_REPORT] Candidate shows strong potential as an 'AI Gateway Engineer' due to direct experience self-hosting and optimizing LLMs and a 'Systems Architect' mindset. However, the role requires 'Go' as the primary language (candidate is Python-heavy) and specific Gateway/Observability tools that are missing. The 'Fast Learner' trait and deep understanding of AI infrastructure mitigate these hard skill gaps, suggesting he can bridge the technology gap quickly.

[ANALYSIS_REPORT] Candidate has strong potential (Fast Learner, self-hosted LLMs/Algo trading implies deep technical/system knowledge + Docker/K8s familiarity), but the resume provided is empty of specific matching keywords. The role is heavily .NET/Azure focused, which is a complete stack mismatch from the inferred Python/Linux background. High 'figure it out' factor but significant re-tooling required for .NET.

[ANALYSIS_REPORT] The resume content is empty. While the candidate's 'Fast Learner' traits (self-hosted LLMs, algorithmic trading) show high technical aptitude, the specific hard skills required for this role (TOTVS Protheus ecosystem, TSS, TAF, and Brazilian Fiscal Obligations) are completely absent. These are specialized, non-adjacent domains to the candidate's stated background, representing a critical gap that 'learning speed' alone cannot immediately bridge for a Level 1 technical support role.

[ANALYSIS_REPORT] Candidate's background in algorithmic trading and self-hosting LLMs demonstrates strong backend capabilities (likely Python-based), API usage, and system administration skills (Docker/Containers) that exceed Junior expectations. While specific enterprise experience is not detailed, the complexity of his projects proves he meets the 'Fast Learner' and technical logic requirements for a Junior Backend role.

[ANALYSIS_REPORT] The resume text is missing, so specific hard skills cannot be verified. However, the candidate's background in 'self-hosted LLMs' and 'algorithmic trading' implies strong competence in Backend Logic, APIs, and Containerization (Docker). The low score reflects the lack of explicit evidence for the specific required stack (Node.js/Nest.js), though the candidate's technical depth suggests they could learn these quickly.

[ANALYSIS_REPORT] The provided resume content was empty. Evaluating solely on the 'Fast Learner' context (Self-hosted LLMs, Algorithmic Trading), the candidate likely has strong engineering fundamentals (probably Python/Linux/Docker context). However, there is zero evidence of the specific hard skills required for this role (Java/Kotlin ecosystem). While the candidate can learn, the role requires established expertise in the Spring/Java stack which is completely missing from the profile.

[ANALYSIS_REPORT] Strong architectural and automation foundation with relevant Python, SQL, and Infrastructure experience. The candidate demonstrates high capacity for complex systems (AI clusters, JIT engines). However, for a 'Senior AWS Data Engineer' role, there are significant gaps in the specific Big Data ecosystem (Hadoop, Spark) and specialized AWS Data services (Glue, DMS). The candidate is a 'Strong Hire' for general engineering but requires ramping up on specific enterprise data tooling.

[ANALYSIS_REPORT] The candidate is a strong match, potentially overqualified. He possesses the exact core domain expertise required (Procurement, Supply Chain, Inventory Management, Logistics) from his roles at Electrolux and Pronutri, where he managed IT procurement for 60 branches. His technical ability (Python, Automation, SQL) far exceeds the 'Spreadsheets' requirement, suggesting he could automate many of the manual tracking tasks described. The missing skills are specific SaaS tools (Oomnitza, Zylo) which are negligible gaps for someone who builds custom inventory systems.

[ANALYSIS_REPORT] The resume content provided within the <resume> tags is empty. While the candidate context indicates a 'Fast Learner' with experience in self-hosted LLMs and algorithmic trading (implying Python proficiency), the specific professional experience required to evaluate the match against Data Engineering tools (Azure, Databricks, Cloudera, SQL) is missing.

[ANALYSIS_REPORT] Candidate has a strong engineering foundation (Mechatronics), excellent scripting (Python/Bash), and solid CI/CD/IaC experience (Terraform). However, the role strictly requires 'Strong' AWS Serverless experience (Lambda, DynamoDB) and AWS-native IaC tools (CDK/CloudFormation), whereas the candidate lists 'AWS Basics' and focuses on on-prem/Terraform. Given the 'Fast Learner' trait, the transition from Terraform to CDK is trivial, but the lack of deep serverless production history is a notable gap.

[ANALYSIS_REPORT] The resume content provided was empty. While the candidate's 'Fast Learner' profile (self-hosted LLMs, algorithmic trading) implies technical aptitude that might cover APIs, the role strictly requires fluency in Portuguese and Spanish and specific client-facing Solution Engineering experience, which are not evidenced in the limited context.

[ANALYSIS_REPORT] The provided resume section is empty. Although the candidate is described as a fast learner with self-hosting and trading experience in the context, no specific technical skills or experience could be verified against the strict Data Engineering requirements (Scala, Spark, Cloud, Medallion Architecture) to justify a score.

[ANALYSIS_REPORT] The resume content is empty. While the candidate has positive demographic and 'fast learner' traits, no hard skills or experience were provided to evaluate against the requirements (Python, AWS, AI/LLM, etc.).

[ANALYSIS_REPORT] Candidate is significantly overqualified for this Level I/Analyst role. He possesses the required Engineering degree (Mechatronics) and Fluent English. His advanced data analysis skills (Python, Pandas, Algorithmic Trading) far exceed the 'Intermediate Excel/Power BI' requirement. While 'Power BI' is a keyword gap, his background in Supply Chain Analytics and Financial modeling ensures he can master it immediately.

[ANALYSIS_REPORT] Candidate matches all core hard skills: Python (Pandas/DataFrames), SQL (transferable to PostgreSQL), AWS familiarity, and CI/CD. The candidate's practical experience with 'production-grade' systems and 'self-hosted AI' demonstrates the required Machine Learning and data pipeline capabilities (ETL). The only deviation is the 'nice-to-have' MSc/PhD academic background, but the MicroMasters and proven 'Fast Learner' ability (self-taught AI/Trading systems) strongly compensate.

[ANALYSIS_REPORT] Candidate provided no resume content in the <resume> tags. Based solely on the 'Fast Learner' context (Self-hosted LLMs, Algorithmic Trading), we can infer technical aptitude and likely familiarity with Docker/Git (common in self-hosting) and possibly Python. However, the role is a Senior Java Architect position requiring a deep legacy and modern Java stack (JSF, Struts, Spring Boot) and Frontend (Angular/React Native) which are not evidenced. The score reflects potential ('Fast Learner') but massive skill gaps for a senior architectural role.

[ANALYSIS_REPORT] Strong alignment with the specific 'AI Focus' of the role, backed by proven experience in self-hosted LLMs and automation (Python). The candidate fits the 'Fast Learner' profile well for architectural concepts. Main gaps are in the traditional enterprise stack (Java/Kotlin, Oracle), but their specialized AI/Infra skills likely outweigh this for this specific strategic role.

[ANALYSIS_REPORT] Strong foundational match in AI/LLMs and Python. The candidate's experience self-hosting LLMs demonstrates deep technical capability that likely exceeds simple API integration (Bedrock/GPT). However, there are specific stack gaps: the resume lists 'AWS Basics' while the role demands specific AWS AI services (SageMaker, Bedrock). Additionally, the candidate focuses on Text/LLMs, while the role explicitly requires Multimodal (Image/Video/Stable Diffusion) experience.

[ANALYSIS_REPORT] Strong domain alignment with the 'Chatbot & IA' requirement due to self-hosted LLM experience, covering Prompt Engineering and NLP concepts deeply. The 'Fast Learner' trait and algorithmic trading background imply strong logic and API integration skills. However, the resume is effectively empty regarding the specific mandatory tech stack (Java, JavaScript) and databases (Oracle, MariaDB), which are critical for a Mid-level (Pleno) role to hit the ground running.

[ANALYSIS_REPORT] Resume text was empty; evaluation relies on 'Fast Learner Context'. Candidate's background in 'Self-hosted LLMs' and 'Algorithmic Trading' provides strong evidence for Python ecosystem, Deep Learning, NLP, and Time Series Analysis requirements. However, critical enterprise-grade skills (SQL, Kubernetes, Cloud Platforms) are not explicitly confirmed in the context, representing significant but bridgeable gaps for a Senior role.

[ANALYSIS_REPORT] Strong match for the specific 'Modern AI' and 'Agentic Systems' requirements. The candidate's background in Quant Trading and Self-Hosted LLMs aligns perfectly with the 'RAG, MCP, and embedding-based semantic search' goals. While he lacks the formal '5+ years Data Scientist' title and specific Big Data keywords (Spark), his founder-level autonomy and deep integration experience suggest he can handle the architectural and implementation demands better than a pure academic Data Scientist. The 'Fast Learner' trait bridges the gap for specific libraries like Scikit-learn.

[ANALYSIS_REPORT] Candidate is a strong Systems/Backend Generalist with proven capacity for complex self-learning (LLMs, Algorithmic Trading). However, the specific tech stack is a mismatch: the role requires Advanced Golang/TypeScript and Event Streaming (Kafka), while the candidate is primarily Python-focused. The foundational skills (Docker, APIs, Architecture) are present, but the specific languages and orchestration tools (K8s vs Local Clusters) represent a learning curve.

[ANALYSIS_REPORT] Candidate is a strong 'Fast Learner' match. While lacking the specific stack (Golang, K8s, Kafka), the JD explicitly allows for a 'proven ability to quickly ramp up' on Golang. The candidate's background in building complex, self-hosted AI clusters and algorithmic trading systems demonstrates the required architectural depth and rapid learning ability. He possesses the core backend requirements (SQL, Docker, API design, Linux) but will need to bridge the gap to Cloudflare's specific toolset.

[ANALYSIS_REPORT] Candidate meets all educational (Engineering) and analytical requirements, significantly exceeding the 'Excel' requirement with Python/Pandas expertise which indicates superior data handling capabilities. Strong background in financial logic and risk management. The primary gaps are specific domain knowledge in Distressed Assets/Bankruptcy (Recuperação Judicial) and explicit deep-dive Accounting proficiency, though the candidate's 'Financial Analyst' experience and 'Fast Learner' trait suggest these can be bridged quickly.

[ANALYSIS_REPORT] The candidate has a strong foundation in Infrastructure-as-Code (Terraform) and system automation (Python), demonstrating the 'Fast Learner' trait via self-hosted AI projects. However, there is a significant stack mismatch: the role requires Node.js and specific Mobile CI/CD experience (App Center, Bitrise), whereas the candidate is Python/Data-centric. Additionally, the candidate lists 'AWS Basics', while the role requires full Cloud Administration.

[ANALYSIS_REPORT] Fundamental domain mismatch. The candidate is a Software/Systems Architect specializing in AI and Supply Chain, while the role is for a Senior Civil/Construction Design Engineer. Despite the Engineering degree, the candidate lacks the entire required toolset (Revit, AutoCAD, BIM) and specific industry experience (Construction, HVAC, Hydraulics) mandated for this Senior-level role.

[ANALYSIS_REPORT] The candidate is a strong technical architect with solid SQL, database, and process automation experience (Python, SDLC), which aligns with the logical requirements of the role. However, the position is specifically for a specialist in the Microsoft Power Platform ecosystem, a domain completely absent from the resume. While the candidate's 'Fast Learner' profile suggests they could master this low-code stack quickly, they currently lack the primary hard skill required.

[ANALYSIS_REPORT] Strong match on core Python experience (6+ years) and engineering mindset. The candidate's background in automation, data pipelines (Pandas/NumPy), and self-hosted infrastructure aligns well with Data Engineering. However, the role leans heavily into Backend Web Development (Django/Flask) and Container Orchestration (Kubernetes), which are not explicitly detailed in the resume. 'AWS Basics' is a potential weakness for a Senior Cloud role, but his 'Fast Learner' trait with complex self-hosted systems suggests rapid upskilling capability.

[ANALYSIS_REPORT] Strong match for the core automation and Python requirements. The candidate is a high-code automation architect (Python, APIs, Docker, AI) capable of complex logic. The primary gap is specific enterprise 'Low-Code' RPA platforms (Power Automate, UiPath) and specific UI automation libraries (Selenium), but given his experience building JIT engines and AI clusters, these are trivial for him to acquire. He exceeds the architectural and integration requirements.

[ANALYSIS_REPORT] The candidate has strong foundational skills in automation (Python), Infrastructure as Code (Terraform), and containerization (Docker), which aligns well with DevOps culture. However, the role is a Senior Specialist position requiring 'Deep practical knowledge' in orchestration (Kubernetes/OpenShift) and API management (Kong/3scale), whereas the candidate lists 'AWS Basics' and 'Local AI Clusters'. While the candidate is a fast learner with self-hosted system experience, the gap between 'Basics' and the deep production expertise required for OpenShift/K8s and Cloud Administration is significant for a senior specialist role.

[ANALYSIS_REPORT] Candidate demonstrates strong architectural ownership and 'figure it out' capability (Self-hosted AI, Trading Bots) which aligns well with the 'Identity Foundation' and architectural requirements. Significant overlap in Domain (SSO, RBAC, Auth). The primary gap is the specific tech stack: the role targets high-concurrency compiled languages (Java/Kotlin/Go) and enterprise message brokers, whereas the candidate is a Python/Scripting expert.

[ANALYSIS_REPORT] The candidate has a strong technical background in Systems Architecture, Cloud/Infrastructure (AWS, Docker, Terraform), and Backend Logic (SQL, Algorithms, Python), which covers many auxiliary requirements. However, the primary hard skill for this specific role—the .NET/C# ecosystem—is completely absent from the resume. While the 'Fast Learner' trait and engineering degree suggest they could learn it (and the role is 'Junior'), the immediate stack mismatch is significant.

[ANALYSIS_REPORT] The candidate has a strong foundation in core infrastructure concepts (Terraform, Docker, Systems Architecture) and a unique advantage for the 'FinOps' requirement due to their background in Financial Analysis. However, the role specifically demands 'In-depth knowledge of Google Cloud Platform' and 'GKE', while the candidate currently lists 'AWS Basics' and local clustering. While cloud concepts transfer, the specific expertise in the Google ecosystem is a significant gap for a Senior Specialist role.

[ANALYSIS_REPORT] Strong match due to the unique combination of 'Mechatronics Engineering' (covering Math/Physics foundations), 'Supply Chain Analytics' (MITx), and proven algorithmic experience (Crypto trading bots, JIT logic). While 'Pattern Recognition' and professional 'Physics' usage aren't explicitly detailed as recent daily tasks, the candidate's engineering background and algorithmic trading experience strongly suggest competence. The logistics domain expertise is a significant value-add for this specific role.

[ANALYSIS_REPORT] Significant domain mismatch. Candidate is a high-level Systems/Cloud Architect (Python, Docker, AWS) applying for a low-level Systems/Embedded role (C++, Drivers, USB). While the Mechatronics degree (2014) and recent AI hardware tinkering suggest potential aptitude and foundational knowledge, the resume lacks professional experience in the core requirements: C++, CMake, and Computer Vision. The 'Fast Learner' trait is strong, but the gap between Web/Data Automation and C++ Device Integration is substantial.

[ANALYSIS_REPORT] Strong match for the Supply Chain and operational efficiency aspects, backed by advanced data engineering skills (Python/SQL) that likely exceed the requested Power Automate/Query proficiency. However, the specific requirement for 'Power BI Advanced' and 'ESG/Sustainability' frameworks represents a tooling and domain gap, though the candidate's 'Fast Learner' profile (self-hosted LLMs, MITx Supply Chain) suggests these would be trivial to acquire.

[ANALYSIS_REPORT] The candidate is a senior-level Backend Architect and Python developer, showing strong aptitude for system design and automation ('Fast Learner'). However, the role is specifically for a Node.js developer with niche experience in Video/HLS. While the candidate shares the 'Backend' domain, the specific language (Node.js) and streaming technology (HLS) are completely absent from the resume.

[ANALYSIS_REPORT] Strong match for the 'AI Transformation' and strategic leadership aspects; the candidate's self-hosted LLM and 'metal-up' infrastructure experience is a high-value fit for the role's core mission. However, a significant technical stack divergence exists: the role requires a Microsoft/Web stack (C#, React, Azure) while the candidate is Python/Linux/AWS focused. Due to 'Fast Learner' traits, the Cloud/DevOps gaps (Azure, Kubernetes) are minor, but the core language mismatch (C# vs Python) is the primary friction point for a Solution Architect role.