Anti-ATS Evaluatorv1.3

[ANALYSIS_REPORT] **TOP** [Copilot: GPT5.1] ATS sees a strong match on Python/Node.js, REST APIs, CI/CD, automation tooling (n8n) and LLM/RAG experience but notes no direct Dialogflow CX or Google Cloud experience. The role is heavily centered on Dialogflow CX and GCP telephony/chat integrations, which are missing but adjacent to the candidate’s LLM automation work. Overall technical depth in conversational AI workflows is good, though tool-specific experience is lacking. **Strengths:** Extensive LLM and RAG pipeline design with multiple providers, Strong Python/Node.js backend and webhook-style development experience, Hands-on n8n and workflow automation background that maps well to agent fulfillment flows **Missing Required:** Direct hands-on Dialogflow CX experience, Hands-on Google Cloud platform experience

[ANALYSIS_REPORT] **TOP** [3-Flash] Strong alignment with the 'builder' and 'vibe coding' philosophy, specifically the use of AI tools (Cursor, Claude) and Python/Node.js stack. The candidate's recent market intelligence project directly mirrors the technical requirements for LLM integration, scrapers, and RAG architectures. Minimal point deduction for lack of deep React/Next.js experience if applying for full-stack, but perfect for the back-end profile. **Strengths:** AI-native development workflow, LLM/RAG implementation, Scrappy/Founder mindset

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] ATS sees general AI/ML and software engineering capability but notes the absence of explicit GPU/parallel-computing and deep-learning framework experience, which are central to this NVIDIA Solutions Architect role. There is also no clear record of customer-facing technical engagements at scale or published technical thought leadership. Overall, it reads more as a systems/automation and LLM engineer than a GPU/DL specialist. **Strengths:** Solid software engineering and systems-architecture background, Real-world AI integration with LLM APIs and RAG, Strong communication skills translating between business and technical stakeholders **Missing Required:** Hands-on GPU and deep learning framework experience

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.1] ATS sees an excellent conceptual match: 4+ years of professional software work, proven LLM integration into a production job intelligence platform, and evident interest in learning/education, with no specific stack constraints. However, the requirement for “4+ years of professional experience as a software engineer” conflicts with the absence of Software Engineer-titled roles. This title mismatch is particularly risky in a high-paying, fully remote role likely to attract many applicants. **Strengths:** End-to-end LLM product integration including RAG, scoring and orchestration, Systems architecture skills well-suited to designing AI-powered workflows and second-brain-style systems, Strong analytical background and demonstrated interest in learning science via MITx MicroMasters **Missing Required:** 4+ years explicitly titled as Software Engineer

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] The candidate matches the Python and AI integration requirements, but the JD leans heavily toward deep Machine Learning Research (TensorFlow, PyTorch, Transformers, MLOps) which is not the candidate's primary focus. The requirement for a CS/Engineering degree is met by Mechatronics, but the lack of 'proven ML training' experience is a gap. The 'Industry 4.0' context, however, aligns perfectly with the mechatronics background. **Strengths:** Mechatronics Engineering degree, Python proficiency, Systems automation experience **Critical Gaps:** Missing deep ML model training/research experience **Missing Required:** Experience with DL frameworks (PyTorch/TensorFlow)

[ANALYSIS_REPORT] **HIGH** [3-Flash] Excellent match for the 'AI/Automation' focus and the Python/Node.js stack. The candidate's experience building custom orchestrators and scrapers fits the 'automation systems' and 'SDK integration' requirements perfectly. The 'Pleno' level is appropriate for the technical depth shown, although the total years of professional experience are much higher. **Strengths:** Automation orchestration, Python/Node.js backend, AI API integration

[ANALYSIS_REPORT] **LOW** [3-Flash] The JD lists 'Experience in development using the Blip platform' as a mandatory technical requirement (Requisitos Técnicos Obrigatórios). The candidate has experience with chatbots and LLMs but no mention of the specific Blip/Take.net ecosystem. ATS will likely auto-reject based on this hard filter. **Strengths:** JavaScript/Python, API Integrations, Agile experience **Critical Gaps:** Missing mandatory Blip platform experience **Missing Required:** Experience with Blip

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] Good alignment on Python, LLMs, and automation. The candidate's background in Finance/Supply Chain matches the 'Data Analyst' title better than 'Software Engineer' roles for ATS purposes. However, the Sicoob environment is likely more traditional/corporate, and the lack of a CS degree (replaced by Mechatronics) might be a slight friction point in ATS scoring. **Strengths:** Finance background, Automation of tasks (n8n), Python/SQL

[ANALYSIS_REPORT] **LOW** [3-Flash] While the candidate has GenAI and Python experience, the role specifically requires experience in video/audio/multimodal data processing (OpenCV, FFmpeg) and media industry challenges. The candidate's scrapers and text pipelines are adjacent but not direct matches for TV content segmentation. **Strengths:** Python, GenAI application, Agile experience **Critical Gaps:** No experience with multimodal video/audio processing **Missing Required:** Hands-on experience with video, audio, or multimodal data

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.1] ATS detects strong overlap on LLM APIs, LangChain, AI pipeline architecture, CI/CD and advanced analytics thinking, but no Snowflake/Cortex focus or formal MLOps/governance framework experience. The role is clearly targeted at Snowflake-centric AI architectures in large enterprises. This makes the profile a good general AI fit but a weaker match for the specific platform-centric requirements. **Strengths:** Hands-on LLM orchestration and RAG system design, Strong Python-based AI integration background, Experience with external LLM APIs, prompt management and evaluation-like variance control **Missing Required:** Deep Snowflake platform experience, Enterprise-grade MLOps and AI governance

[ANALYSIS_REPORT] **TOP** [3-Flash] Exceptional match for the 'Builder' profile and the technical stack (n8n, PostgreSQL, AI Agents). The candidate's recent project is essentially a more technical version of what is requested here. The 'SDR with IA' focus aligns perfectly with the candidate's market intelligence platform. **Strengths:** n8n expertise, PostgreSQL depth, AI Agent architecture

[ANALYSIS_REPORT] **LOW** [3-Flash] The role requires 'Sólida experiência em SAP S/4HANA' and 'SAP Activate'. The candidate has experience with TOTVS/Protheus, which is a competing ERP, but SAP is a specific ecosystem with high barriers to entry for non-certified/non-experienced candidates. ATS will block on the 'SAP' keyword. **Strengths:** ERP knowledge (TOTVS), IA application, English fluency **Critical Gaps:** Missing mandatory SAP ecosystem experience **Missing Required:** Solid experience in SAP S/4HANA

[ANALYSIS_REPORT] **HIGH** [3-Flash] Strong match for the Python/SQL/TypeScript stack and the focus on Vector Databases (Pinecone/pgvector). The candidate's market intelligence project used PostgreSQL and AI scoring, which translates well to these requirements. The 'Junior/Pleno' level is a safe bet for a career-changer with strong technical proof-of-work. **Strengths:** Vector Database concepts, Python/TypeScript proficiency, Agent development

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.1] ATS will see a decent match on years of experience, AI/automation work, supply-chain/production context and leadership of an engineering-heavy operation. However, there is no explicit record of managing enterprise-scale AI teams or using mainstream AI frameworks like PyTorch/TensorFlow and Databricks/AWS ML services. The profile reads more as a senior hands-on architect/lead than a seasoned enterprise AI engineering manager. **Strengths:** 12+ years blending operations, supply chain and automation relevant to the role’s domain, Recent end-to-end AI product delivery (job intelligence platform) with strong architecture and reliability, Demonstrated ability to translate complex operations into digital products and KPIs **Missing Required:** Hands-on experience with mainstream AI frameworks and cloud AI services at enterprise scale

[ANALYSIS_REPORT] **LOW** [3-Flash] The role is for a 'Junior' level and requires C#/.NET, which is missing from the candidate's profile. While the candidate is strong in Python and IA, the C# requirement is central to the integration tasks described. The candidate is also likely overqualified for a 'Junior' role. **Strengths:** Python proficiency, IA application, RAG experience **Critical Gaps:** Missing mandatory C#/.NET experience **Missing Required:** Practical experience with C# and .NET

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] The candidate has the required '8+ years' of IT experience and strong GenAI skills (Bedrock is an Amazon service, candidate uses Gemini/Claude which are similar). However, the role is 'Solutions Architect' at AWS, which typically requires deep AWS platform knowledge and a history of 'SA' titles. The 'Software Engineer' title gap will hurt in ATS. **Strengths:** LLM/GenAI depth, Systems Engineering background, English fluency **Critical Gaps:** No specific AWS Solutions Architect experience **Missing Required:** 8+ years specific domain experience, Experience in consulting/architecture

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.1] ATS notes strong GenAI and agentic-system adjacency via LLM pipelines, orchestrators and automation, but does not see explicit Kubernetes, SageMaker, AWS ML or Terraform experience. The role is heavily MLOps/AI Ops-focused with specific AWS tooling expectations. This makes the profile a partial fit on concepts but underweight on the named infrastructure stack. **Strengths:** Real-world GenAI pipeline work with variance control and monitoring concepts, Experience building agent-like orchestrators coordinating multiple tools and processes, Strong Git/CI-CD habits and Docker/container experience **Missing Required:** Hands-on K8s-based MLOps stack, SageMaker/AWS ML experience in production

[ANALYSIS_REPORT] **HIGH** [3-Flash] Strong match for a Full Stack IA Engineer role. The requirements for Python, RAG, Vector Databases, and LLMs are all present in the candidate's recent work. The candidate's ability to handle 'architecture from frontend to backend' is proven by their Market Intelligence platform. **Strengths:** RAG implementation, Full Stack delivery, Scraper/OCR experience

[ANALYSIS_REPORT] **TOP** [3-Flash] Exceptional match for the 'AI CoE Lead' role. The specific tasks—developing a CLI for agents, runtime of agents, LLM Gateways, and monitoring—are exactly what the candidate built for their own market intelligence platform. The 'AI-Native' requirement (daily use of Cursor/Copilot) is a direct hit. **Strengths:** CLI development experience, LLM Orchestration/Gateway logic, AI-Native workflow

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] ATS sees general AI/software ability but finds almost no match on the core requirements: GPU/parallel-computing, CUDA, deep-learning frameworks, and energy-industry experience with ML on energy data. This role targets a specialized energy-domain AI architect, while the profile is oriented toward LLM systems and business operations. That is a critical domain and tooling mismatch. **Strengths:** Solid general AI/automation engineering background, Strong analytical and systems-thinking skills, Good communication skills for technical audiences **Critical Gaps:** No experience applying data science or deep learning to the energy industry, No GPU/CUDA-based high-performance computing background **Missing Required:** 8+ years of GPU-aware data science/software development, Deep learning framework experience on industry (energy) data

[ANALYSIS_REPORT] **TOP** [3-Flash] Duplicate/Similar to Job 14. Perfect match for the same reasons: CLI development, LLM Gateways, and AI-native workflow. The candidate's background in 'Constraints-based engineering' is ideal for the 'governance and cost control' aspects of this CoE role. **Strengths:** AI Orchestration, Node/Python proficiency, Cost optimization mindset

[ANALYSIS_REPORT] **HIGH** [3-Flash] Good alignment with GFT's requirement for RAG, chatbots, and agents in production. The candidate's experience with indexation, vector bases, and scrapers is a direct match. GFT's focus on 'International Career' also matches the candidate's EU citizenship and C2 English. **Strengths:** RAG/LLM implementation, Python/Node expertise, English C2

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] IBM Consulting roles are heavily standardized. While the candidate has the Python/IA skills, the ATS will likely flag the lack of 'Data Engineer' in the title history. The requirement for 'Scikit-learn/TensorFlow' also suggests a more traditional ML path than the candidate's LLM/API-centric path. **Strengths:** GenAI experience, JavaScript integration, English fluency **Critical Gaps:** Title mismatch for 'Data Engineer' role **Missing Required:** Experience with ML libraries like Scikit-learn/TensorFlow

[ANALYSIS_REPORT] **TOP** [3-Flash] Strong match for an 'Architecture Manager' role focused on Multi-agents and RAG. The candidate's 'Trusted Advisor' profile and 'Bridge' experience between business and tech are exactly what CI&T seeks. The technical stack (Python/Node/LLMs) is a perfect fit. **Strengths:** Multi-agent system concepts, RAG architecture, Technical leadership

[ANALYSIS_REPORT] **LOW** [3-Flash] Duplicate of Job 17. Same constraints: IBM Consulting, traditional ML framework requirements, and 'Data Engineer' title mismatch. **Strengths:** GenAI, English, JavaScript **Critical Gaps:** Title mismatch **Missing Required:** Scikit-learn/TensorFlow