[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a strong profile for a senior backend role, demonstrating over 10 years of experience in system architecture, automation, and delivering complex, ROI-focused solutions. Their background as a founder and independent consultant highlights significant autonomy, problem-solving capabilities, and a proven track record of rapidly acquiring new technical skills, such as self-hosting LLMs and algorithmic trading. They possess solid experience with APIs, Docker, CI/CD pipelines, Git, and a strong understanding of performance and system resilience, aligning well with many core requirements. The primary gap is direct experience with Golang; however, their demonstrated 'fast learner' ability and existing backend expertise suggest a high potential for rapid adoption. Other minor gaps exist in specific differentiator technologies like production Kubernetes, DDD, messaging queues, and observability tools, but their foundational infrastructure and development skills are robust.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate presents a strong profile with extensive experience in system architecture, automation, data manipulation (Python/Pandas), and building production-grade solutions. Their background as a founder and freelance engineer highlights significant problem-solving, analytical skills, and a proven capacity for rapid self-learning, directly addressing the 'proactivity and disposition to learn' requirement. Key strengths include understanding of data pipelines (CI/CD), business-focused system design, and the ability to optimize complex processes. However, the primary gaps lie in direct hands-on experience with specific Google Cloud Platform (GCP) services, particularly BigQuery and Looker, which are central to the role. While the candidate has basic AWS knowledge and a demonstrated ability to master new infrastructure, the lack of prior exposure to these specific tools is a noticeable deficiency. The absence of explicit experience with Google Sheets for data manipulation is a minor point, easily bridged by their broader data skills.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong capabilities in AI infrastructure and system automation, with a proven track record of self-hosting LLMs and building complex systems. This aligns well with the 'AI-driven content generation' and 'automate what matters' aspects of the role. However, there are significant gaps in 'must-have' requirements: native Spanish proficiency, explicit experience in writing technical content/SEO blog posts for 2+ years, and understanding of the Mexican market's content consumption habits. While the candidate is a fast learner, these core requirements for an 'AI Content Engineer' focused on the Mexican market are not met.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a capable Systems Architect and rapid learner (self-hosted AI, algo trading) with relevant backend/infrastructure experience. However, the role strictly requires 'Expertise' in Go and AWS/Kubernetes (EKS), whereas the candidate is Python-centric and explicitly lists only 'AWS Basics'. The domain fit is good (Backend/Systems), but the specific tech stack gap is significant for a senior/expert role.

[ANALYSIS_REPORT] [G2.5 Flash] The candidate demonstrates strong foundational skills in cloud infrastructure (AWS Basics, Docker, Terraform), system architecture, and development (Python, Shell Scripting, SQL), indicating a solid ability to build and manage robust systems. Their experience with SSO and RBAC reflects an understanding of security principles. However, the resume lacks explicit experience with specific infosec testing tools, offensive cloud security practices, and direct familiarity with frameworks like OWASP Top 10 and MITRE ATT&CK. Furthermore, while the candidate's general systems experience is relevant, specific exposure to Security Information and Event Management (SIEM), Cloud Security Posture Management (CSPM), and Cloud Workload Protection Platform (CWPP) tools, which are critical for the monitoring and triage responsibilities of this role, is not explicitly evident. While the candidate's proven "fast learner" ability mitigates some of these gaps, they remain direct areas for development in a Cloud Security Analyst position.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core data engineering and Python programming requirements, supported by a background in complex system architecture and 'Fast Learner' traits (e.g., algorithmic trading, self-hosted LLMs). The candidate's engineering degree and English fluency align perfectly. The primary gaps are operational/support-specific tools (ServiceNow, ITIL) and specific SQL Server optimization evidence, which are low-barrier to learn given the candidate's technical depth. The candidate may be overqualified for the support-focused aspects of the role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on engineering fundamentals (Python, SQL, APIs, CI/CD) and infrastructure (Docker, Terraform). The candidate's experience building automated JIT engines and algorithmic trading bots demonstrates the required data workflow logic. While he lacks specific 'Modern Data Stack' keywords (dbt, Snowflake), his background in self-hosted AI and Pandas/NumPy indicates a high capacity to bridge these gaps quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is significantly overqualified for a 'Junior' role, possessing advanced proficiency in Python, Automation, APIs, and AI Infrastructure that exceeds requirements. His experience building end-to-end systems (JIT inventory, Trading Bots) proves strong data pipeline capabilities. The only explicit keyword gap ('Data Lakes') is easily bridgeable given his background in 'Local AI Clusters' and 'Supply Chain Analytics'.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on core engineering and Python/Pandas automation, which provides a solid foundation for Data Engineering. The candidate's background in Quant and Supply Chain proves ability to handle complex logic and ETL concepts ('JIT inventory', 'risk algorithms'). While specific Big Data tools (Spark, Databricks) are missing, the Job Description explicitly accepts 'interest' in them, and the candidate's 'Fast Learner' profile (self-hosted LLMs) suggests rapid adaptability. The primary gap is the explicit omission of SQL and specific database technologies.

[ANALYSIS_REPORT] [Model: gemini-cli] Exceptional domain alignment: The candidate's specific background in Supply Chain/Logistics (Electrolux, Pronutri) combined with the required technical stack (SQL, APIs, Python) makes them a near-perfect fit for a Supply Chain AI company. The 'Founder' and 'Consultant' experience strongly validates the required ability to bridge technical and non-technical stakeholders ('Solutions Engineer' core competency). The only gaps are specific named ERP ecosystems listed as bonuses, and structured support workflows.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is an overqualified Architect with strong Linux, Automation (Python/Shell), and 'metal-up' hardware skills that cover the Linux/DevOps portion of the role exceptionally well. The 'Fast Learner' trait and self-hosting experience mitigate gaps in specific tools like pfSense or specific switch brands. However, the resume lacks evidence of the Microsoft ecosystem (Windows Server, Active Directory, PowerShell) and enterprise virtualization (VMware/Citrix), which appear to be a significant operational component of this specific support role.

[ANALYSIS_REPORT] [Model: gemini-cli] High potential match despite toolset mismatch. The candidate is an expert in the underlying concepts (Automation, LLMs, API Integrations, Business Logic) using a 'Code-First' stack (Python, Docker) rather than the required 'Low-Code' stack (Microsoft Power Platform). Given his proven track record of building complex, self-healing inventory systems and self-hosting LLMs, he likely understands the *mechanics* of the required tasks better than a typical low-code user and could master the Power Platform tools extremely rapidly. The deduction is strictly for the lack of immediate familiarity with the specific Microsoft ecosystem tools.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate exceeds all core hard skills (English, Process/Systems experience, Tech familiarity). Extensive background in process automation (JIT, Python) and system integration aligns perfectly with Solutions Engineering goals. Only misses the specific 'nice-to-have' platform knowledge (Pipefy) and Spanish language skills.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate has the required Engineering degree and relevant Supply Chain/Industrial background (Electrolux), which aligns with resource planning and critical analysis. However, the role is a heavy Project Management/Planning position (PMB, Shutdowns, Team Leadership) requiring a specific Post-Grad, whereas the candidate's recent profile is heavily specialized in Technical Architecture, AI, and Automation (Builder vs. Manager).

[ANALYSIS_REPORT] [Model: gemini-cli] Strong cultural and domain fit (Risk/Finance/Crypto background aligns with Fraud Prevention). Candidate meets the 'Extreme Ownership' criteria as a Founder/Architect. Strong Python skills satisfy the primary language requirement, though the specific backend stack is Go. 'Fast Learner' trait (Self-hosted LLMs, Algo Trading) mitigates the lack of explicit enterprise-scale ML library keywords (PyTorch/Scikit-learn).

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong 'Fast Learner' match with proven ability to master complex systems (AI clusters, algorithmic trading) and meets the core scripting (Python) and container (Docker) requirements. However, there is a significant gap in the specific enterprise-grade orchestration and Red Hat ecosystem tools (OpenShift, Kubernetes, Ansible) required for this consulting role. His background suggests he can bridge these gaps quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate exceeds the 'Plus' requirements (SQL, Finance background) and possesses deep technical context essential for managing engineering delivery. While the specific keywords 'Jira' and 'Agile' are missing, his experience as a Founder and Solutions Architect proves he can manage scope, delivery, and ambiguity. Tooling gaps are negligible for his demonstrated learning velocity.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate demonstrates strong engineering maturity, architectural thinking, and relevant 'Infrastructure-as-Code' skills (Terraform, Docker, Python) that align well with a Solution Architect profile. However, the role strictly requires 'Mastery' (Domínio) of the AWS ecosystem (Serverless, Networking, Databases), whereas the candidate explicitly lists 'AWS Basics'. His experience with self-hosted clusters and automation suggests a rapid learning curve, but the immediate gap in specific AWS managed services is significant.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong Systems Architect with impressive self-taught infrastructure skills (local AI clusters, automated JIT systems) and automation experience (Python, CI/CD), indicating high aptitude. However, this is a specialist 'Observability' role requiring deep, practical experience with the **Grafana Stack** and **Kubernetes**, neither of which are explicitly listed in the resume. While he understands the concept of 'self-sustaining systems' (which implies monitoring), he lacks the specific hard skills in telemetry pipeline design and the requested cloud providers (Azure/GCP) to be a 'plug-and-play' mid-level specialist.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a highly capable Solution Architect with strong skills in automation, self-hosted infrastructure (Docker, GPU clusters), and backend logic (Python). However, this specific role is a specialized Platform Engineering position strictly requiring 3+ years of deep experience in Apache Kafka and Debezium (CDC). The candidate's resume completely lacks references to Event Streaming, Message Brokers, or Kubernetes Operators. While the candidate has the technical aptitude ('Fast Learner', 'Infrastructure') to learn these, the role requires immediate seniority in a domain that is currently missing from their profile.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate has strong Data Engineering and ETL fundamentals via Python/Pandas and strong business logic (Supply Chain/Finance). However, the role strictly emphasizes 'Alteryx' (a specific low-code ETL tool) which is missing. SQL is also not explicitly listed, though likely implied by his backend/quant background. Highly capable of learning Alteryx rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is an exceptionally strong architectural match, offering robust 'code-first' automation capabilities (Python, Custom APIs, Self-hosted LLMs) that surpass the requested low-code requirements. His 'System Integrator' background aligns perfectly with the process mapping needs. While he lacks specific low-code/SaaS keywords (Zapier, ClickUp), his deep technical expertise in 'metal-up' automation and AI infrastructure indicates he can master these tools rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong technical match for the engineering and architectural demands (Python, Docker, Infrastructure, Systems Design) and demonstrates deep 'metal-level' understanding of LLMs (self-hosting, optimization). The 'Fast Learner' trait suggests he can bridge the gap to specific tools quickly. However, strictly comparing against 'Essential' requirements reveals gaps in the specific AI Application stack: there is no explicit mention of RAG architectures, Vector Databases, or the specific orchestration tool 'n8n', and his 'bot' experience is in trading algorithms, not conversational interfaces (Chatbots).

[ANALYSIS_REPORT] [Model: gemini-cli] Strong conceptual fit. The candidate's 'metal-up' experience with self-hosted AI clusters and complex automated systems demonstrates the requested 'Fast Learner' trait and deep technical capability. While lacking specific enterprise stack keywords (Azure, C#) and high-level wrappers (LangChain), the candidate's foundational knowledge in Python, Infrastructure, and AI internals suggests these gaps can be closed rapidly. The 'Independent Consultant' background aligns well with the client-facing requirements.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong domain alignment (Finance/Crypto) and high aptitude for automation (Self-hosted AI, Trading Bots). However, there is a significant tech stack mismatch: the role requires a TypeScript/PHP ecosystem specialist (Vitest, Cypress, PHPUnit) for dedicated QA, whereas the candidate is a Python/Infrastructure expert. While the candidate fits the 'Automation' aspect, the specific languages and testing frameworks are absent.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong technical alignment with the 'Nice to Have' stack (Cloud, CI/CD, DevOps) and excellent business/process optimization background (JIT, Supply Chain) which fits the 'Process Improvement' requirements. The score is capped because the role heavily emphasizes formal Team Management (recruiting, performance reviews, leading dev teams), while the candidate's recent experience is primarily as a Solopreneur/Freelancer without explicit evidence of managing direct reports.

[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.