[ANALYSIS_REPORT] Strong match due to direct Supply Chain domain expertise overlapping with the 'Supply Analytics' team focus. Candidate exceeds technical requirements for Python, SQL, and Automation ('Hacker's mindset'). The background in algorithmic trading and financial analysis covers the statistical modeling requirements. Primary gap is specific BI visualization tools (Tableau) and formal A/B testing frameworks, though the candidate's engineering background suggests rapid upskilling.

[ANALYSIS_REPORT] Strong foundational match in Automation, Scripting (Python/Bash), and IaC (Terraform). The candidate's background as a 'Systems Architect' and Founder building 'self-sustaining' JIT systems aligns perfectly with the SRE goal of operational resilience and automation. The 'Fast Learner' trait (self-hosted AI clusters) suggests high aptitude for closing specific tool gaps. Main deductions are for the lack of explicit 'Mastery' in Kubernetes (JD requires 'Domínio') and the listing of only 'AWS Basics' versus the requested Multi-cloud/Senior depth. Observability tools are a keyword gap but conceptually covered by his risk/monitoring experience.

[ANALYSIS_REPORT] [Model: gemini-vertex] Significant platform mismatch. The candidate is a Linux/DevOps-focused Architect with strong automation skills (Python), whereas the role is a classic Windows System Administrator position requiring deep troubleshooting of Active Directory and Exchange. While the candidate is a 'Fast Learner' and could likely learn PowerShell, the requirement for 5+ years of specific Windows ecosystem experience for a high-volume ticket role constitutes a major gap.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate is a strong technical match regarding Python, Pandas, and automation logic, with a proven "Fast Learner" profile (Algo trading, self-hosted infrastructure). However, the role is specifically for a *Senior* Data Engineer deep in the Google Cloud (GCP) ecosystem. While the candidate understands infrastructure (Terraform, AWS Basics) and data manipulation, they lack specific experience with the requested GCP suite (BigQuery, DataFlow, Composer) and formal Enterprise Data Warehousing patterns (Medallion Architecture). They are overqualified in problem-solving but would require a ramp-up period for the specific GCP toolset.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate possesses relevant domain knowledge in Supply Chain and Finance (OTC context) and has strong technical skills in API integration. However, the role specifically demands a 'Senior' Functional Analyst with deep, specific expertise in Oracle EBS modules (AR, AP, OM), which is not present in the resume. The candidate's profile is heavily skewed towards Technical Architecture and Engineering (Python, AI, Infra) rather than the Functional ERP analysis required.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate is a strong Systems Architect with excellent Infrastructure and Python automation skills, fitting the 'Fast Learner' profile. However, this is a Senior Staff Full-Stack role requiring deep experience in Client-side development (React/Vue) and Web Frameworks (MVC), which are completely absent from the resume. While the candidate can handle the Backend and Cloud/Infrastructure aspects effectively, the lack of Frontend experience and specific software engineering methodologies (TDD) creates a significant gap for a Principal-level leadership role in a web-product company.

[ANALYSIS_REPORT] [Model: gemini-vertex] Significant domain mismatch. The candidate is a technical Solutions Architect/Engineer focused on 'building' (Python, Docker, AI, JIT Systems), while the role is a functional Business Analyst position focused on 'facilitating' (Process Management, Teams Ideas platform, Workshops). While the candidate's analytical skills are transferable, they lack the specific Product Management frameworks and the requested Microsoft 365 administrative expertise.

[ANALYSIS_REPORT] [Model: gemini-vertex] Candidate is a strong match for the underlying engineering logic, possessing deep experience in Python-based data manipulation (Pandas/NumPy), automation, and system architecture. The 'Fast Learner' trait (Self-hosted LLMs, Quant Trading) strongly suggests he can rapidly adapt his local/scripted data expertise to the required distributed Big Data stack (PySpark). However, significant tooling gaps exist: he lacks specific experience with the Databricks ecosystem, the Azure cloud environment, and enterprise messaging systems (Kafka), requiring a ramp-up to translate his backend skills to large-scale ETL pipelines.

[ANALYSIS_REPORT] The candidate is a 'Code-First' Automation Architect (Python/Shell), whereas the role is strictly for a 'Low-Code' Developer (Microsoft Power Platform). While the candidate's fundamental logic and engineering skills likely exceed the role's requirements (suggesting rapid mastery), the specific proprietary ecosystem experience is completely absent.

[ANALYSIS_REPORT] The candidate demonstrates strong 'fast learner' traits and architectural competence (Systems Integrator, Founder, Python Automation), aligning well with the role's innovation culture. However, the position specifically demands proficiency in strongly typed OOP languages (C#, Java, or Kotlin) and experience with 'large-scale SaaS' environments, whereas the candidate is primarily Python-focused with a background in self-hosted/indie systems and finance. The transition from Python/Scripting to a Senior C#/Java SaaS role is a significant gap despite the adjacent backend knowledge.

[ANALYSIS_REPORT] Major technical mismatch. While the candidate has strong domain expertise in Finance, Supply Chain, and ERP concepts (Business Logic), they lack the entire required specific tech stack (Microsoft Ecosystem). The role strictly requires 5+ years of Dynamics 365, X++, and C#/.NET development, whereas the candidate is a Python/Linux/AI infrastructure specialist. The domain knowledge matches, but the hard engineering skills do not overlap.

[ANALYSIS_REPORT] Strong match for the GenAI and Python engineering aspects. The candidate's experience with self-hosted LLMs and algorithmic trading demonstrates the required 'Fast Learner' trait and deep technical capability. Major gaps are specific Big Data frameworks (Spark/BigQuery) and the specific functional focus on Data QA automation, though his systems background suggests high adaptability.

[ANALYSIS_REPORT] The candidate has a strong foundation in data logic, Python/Pandas, and on-premise infrastructure, evidenced by building complex self-hosted systems (JIT inventory, trading bots). This demonstrates the core competency of a Data Engineer ('figuring it out' and building pipelines). However, there is a significant gap in the specific toolset requested (Airflow, dbt, NiFi). While the candidate performs data engineering tasks using Python scripts, they lack experience with the industry-standard orchestration and transformation frameworks specified.

[ANALYSIS_REPORT] Candidate has strong foundational alignment with the 'Analytics Engineer' role through proven experience in Python (production/automation), SQL, and complex data logic (JIT systems, Algo trading). While specific stack keywords like 'dbt' and 'Airflow' are missing, the candidate's track record of building 'end-to-end logistics logic' and 'custom automation pipelines' demonstrates mastery of ELT concepts. Given the 'Fast Learner' trait and self-hosted infrastructure background, learning these specific tools would be trivial.

[ANALYSIS_REPORT] Strong match in core domain (Finance/Banking) and primary language (Python). The candidate's background in quantitative analysis (Pandas/NumPy) and self-hosted infrastructure indicates a high aptitude for transitioning to Data Engineering. While missing specific Big Data ecosystem tools (Spark, Hive, NiFi), the candidate's 'Fast Learner' profile and engineering degree suggest these can be rapidly acquired.

[ANALYSIS_REPORT] Strong match on foundational engineering, automation (Python/Shell), and IaC (Terraform). The candidate's 'metal-up' and self-hosting experience proves deep technical aptitude, and the 'Fast Learner' trait suggests rapid adaptation to specific cloud tools. However, the role is a 'Senior' position specifically for AWS/Azure, and the candidate explicitly lists only 'Basic AWS' knowledge. He lacks specific enterprise observability experience (DataDog/CloudWatch) and formal large-scale SRE incident management (War Rooms) required for a bank's command center, though his diverse background mitigates this risk.

[ANALYSIS_REPORT] Strong architectural and backend engineering foundation. The candidate matches the 'Senior/Lead' profile with proven ability to build complex, self-healing systems (JIT, AI Clusters) and bridge business needs. However, there is a significant stack mismatch: the role requires advanced Golang/Typescript and Kafka/Kubernetes (Cloud Native stack), while the candidate is deeply specialized in Python and bare-metal/local infrastructure. High potential to learn, but lacks the immediate 'Specialist' fluency in the required languages.

[ANALYSIS_REPORT] Candidate has a strong engineering foundation (Python, Terraform, Docker) and 'Fast Learner' traits proven by self-hosted AI/Trading systems. However, the role is heavily specialized in Observability (Datadog, OpenTelemetry) and Kubernetes orchestration, which are absent from the resume. He fits the 'Builder' profile but lacks the specific Enterprise SRE toolchain experience.

[ANALYSIS_REPORT] Strong domain fit with Crypto/Finance background and 'Fast Learner' ability (AI/LLM clusters). However, significant gaps in specific tech stack (Coinbase is heavy on Go/Ruby, candidate is Python) and organizational scale (Freelance/Founder vs. 'Staff' level distributed systems at a major public company).

[ANALYSIS_REPORT] The candidate is currently a Systems Architect/Engineer utilizing Python and AI, whereas the role is a traditional Corporate FP&A position. While he possesses strong analytical capability (Quant/Pandas) and past financial experience (2018), he lacks the specific academic background (Admin/Accounting) and recent hands-on experience in corporate accounting cycles (Budgeting, Cash Flow) requested. He is technically overqualified but domain-misaligned.

[ANALYSIS_REPORT] Strong match. The candidate's background in 'Self-hosted LLMs' and 'Privacy-focused AI deployments' demonstrates a depth of AI knowledge that exceeds the requirement for simply 'using' Copilots. His Python expertise and Engineering degree align perfectly. The 'Privacy' focus maps directly to the AI Security/Governance requirement. The primary gap is specific enterprise tool experience (M365), but his technical AI depth compensates heavily.

[ANALYSIS_REPORT] Strong architectural and technical match. Candidate satisfies all strict requirements: Docker, multi-file project architecture (Python/JIT systems), and Latin America location. The 'Fast Learner' trait (Self-hosted LLMs) is highly relevant to the AI/AfterQuery context, bridging the gap for the specific 'task-based' nature of the role. The only notable keyword gap is explicit experience with 'Testing Frameworks' (required for the 'deterministic test suites' responsibility), though this is partially implied by 'CI/CD Pipelines' and 'Production-grade' experience.

[ANALYSIS_REPORT] The candidate is a highly capable Systems Architect and Python developer, but this role requires deep, specific expertise in the Microsoft 365 ecosystem (SharePoint, Power Platform). There is effectively zero technical overlap between the candidate's open-source/AI stack (Python, Linux, Docker) and the job's enterprise requirement (SharePoint, SPFx, Power Automate).

[ANALYSIS_REPORT] The candidate is a strong technological match for the 'Autonomy' and 'Complex Systems' aspects of the role, with relevant experience in Data (Python/Pandas) and Infrastructure (Docker/Terraform/Self-hosted AI). However, there is a significant hard skill gap: the position explicitly requires experience with **Go (Golang)** and mastery of **PostgreSQL** (optimization/modeling), whereas the candidate's stack is heavily Python-centric. His proven 'Fast Learner' ability (self-hosting LLMs, algorithmic trading) suggests he could adapt, but he lacks the immediate language proficiency required.

[ANALYSIS_REPORT] Significant technology stack mismatch. The candidate is a senior Python/Infrastructure specialist (Solutions Architect) with a focus on Automation and Data, whereas the role specifically requires a Java/Angular Fullstack developer. While the candidate has strong general engineering experience and 'Fast Learner' traits, the absence of the core programming language (Java) and the specific frontend framework (Angular) constitutes a domain gap that would require substantial re-skilling.

[ANALYSIS_REPORT] High alignment in core domains (AI/LLMs and FinTech/Trading) and System Design. However, the candidate lacks the specific required tech stack (.NET/C# and Frontend frameworks) and specific Product Management tooling (Jira, Amplitude). The 'Founder' experience partially offsets the lack of formal PM title years, but the distinct stack gap remains.

[ANALYSIS_REPORT] Candidate is a high-potential 'Fast Learner' with strong fundamental Engineering skills (Python, SQL, Terraform, Automation) and proven success building complex data-driven systems (JIT inventory, Algo trading). While the candidate lacks experience with the specific Enterprise Data stack requested (Snowflake, Airflow, Kafka), their background in 'metal-up' architecture and self-hosted clusters suggests they can rapidly master these tools. The gap is purely tooling-specific, not a lack of domain capability.

[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.