Anti-ATS Evaluatorv1.3

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This role requires Masters and/or PhD with publication history in AI-focused areas (CS, Statistics, Math, Computer Engineering, Electrical Engineering). Candidate has a Bachelor's in Mechatronics with no graduate degree and no academic publications. This is a hard filter — the academic credential requirement is fundamental and non-negotiable for a research position. Even the technical requirements (Deep Learning, Reinforcement Learning, NLP at research depth) go far beyond the candidate's applied AI integration experience. To improve: this role is not a realistic target without graduate-level education. **Strengths:** English advanced, AI Generativa applied experience, Prompt engineering **Critical Gaps:** Masters/PhD required (hard filter — candidate has Bachelor's only), Research publication history required (none), ML/DL research expertise (applied AI ≠ research AI) **Missing Required:** Masters or PhD, Publication history, Deep Learning research, Reinforcement Learning, NLP research

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Candidate has Python, data analysis background (supply chain analytics, trading), AI pipeline experience, and cross-functional collaboration. However, the JD requires ML model development and training (not just API integration), deep learning, NLP, and large-scale data analysis — the candidate's AI experience is integration-focused (calling APIs) rather than model training. The 'formação superior em ciência da computação, engenharia de software, inteligência artificial ou áreas correlatas' may or may not accept Mechatronics. The role frames AI Generativa as model development, not API consumption. To improve: highlight any ML experimentation, emphasize the statistical analysis from trading and supply chain work, and detail the prompt engineering pipeline as model behavior optimization. **Strengths:** Python proficiency, AI Generativa applied experience, Cross-functional collaboration and multidisciplinary work **Critical Gaps:** ML model development and training (core role requirement — candidate does API integration, not model building) **Missing Required:** ML model training experience, Deep Learning, NLP

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.2] Candidate aligns very well with LLM prompt design, extraction/classification pipelines, validation logic, and production deployment collaboration signals. The main ATS weakness is the stated 3+ years with LLM-based systems (resume shows strong work but mostly since 2025). Improve by clearly dating any earlier LLM/prod NLP work if it exists, and by adding a short "LLM extraction" case study with accuracy metrics and schema examples. **Strengths:** prompt engineering for classification/extraction, validation rules + data quality mindset, production-grade system delivery **Missing Required:** 3+ years of experience with LLM-based systems

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Candidate has Python, system architecture, CI/CD, data pipelines, and business-to-tech translation which align well with the role's core needs. However, the JD requires 8+ years of professional software engineering experience (candidate has 0 years with SE title), solid AWS experience with specific services (EventBridge, SQS, Lambda, RDS — candidate lists only 'AWS fundamentals'), and Kubernetes experience (candidate has Docker but not K8s orchestration). To improve, candidate should highlight Go/Python production systems explicitly as 'software engineering,' add specific AWS service experience if any exists, and emphasize the microservices-adjacent architecture of the orchestrator project. **Strengths:** Python and system architecture skills, Data pipelines and automation experience, Business-to-technical translation ability **Missing Required:** 8+ years software engineering experience, solid AWS experience with specific services, Kubernetes, microservices architecture

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.2] Candidate matches Go backend building, APIs, SQL/PostgreSQL, Linux, Docker, CI/CD, and has delivered production systems end-to-end. ATS will likely flag missing explicit Kubernetes and limited explicit cloud platform depth, plus lack of standard "Backend Engineer" titles. Improve by adding a dedicated Backend section: REST APIs built, auth, DB schema, perf/monitoring, and any Kubernetes or cloud deployment details (even small). **Strengths:** Go backend + systems architecture, PostgreSQL/SQL, Docker + production ops mindset **Missing Required:** Kubernetes (explicit experience), cloud platform depth (AWS/GCP/Azure)

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.2] Candidate fits the meta-skill: AI-assisted development with disciplined review, multi-file system ownership, and converting requirements into executable specs. ATS will penalize lack of explicit "GitHub Copilot as primary environment" and unclear 5+ years professional software development framing. Improve by adding a short section naming Copilot/Copilot CLI usage (if true), plus concrete examples of coordinating AI across a codebase with tests, refactors, and architectural constraints. **Strengths:** context/spec engineering, human-in-the-loop safety mindset, backend + infrastructure breadth **Missing Required:** 5+ years professional software development experience (clearly evidenced), deep experience using GitHub Copilot as primary environment

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] This is a specialist RL research role (PyTorch + large-scale RL experiments + online RL methods like GRPO), which the resume does not demonstrate. ATS will not treat LLM application building or automation as transferable to RL research depth. Improve only by building and publishing RL projects (PyTorch + frameworks + experiment logs) and demonstrating real RL experimentation at scale. **Strengths:** systems thinking, production engineering discipline, automation and optimization mindset **Critical Gaps:** Reinforcement Learning R&D (core domain), large-scale RL experiments/online RL (GRPO) **Missing Required:** deep RL algorithms expertise, PyTorch RL pipeline experience

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Candidate has Python, SQL, data pipelines, and some ML-adjacent work (LLM scoring pipeline, prompt tuning with PID-inspired approach, embedding models). However, the role requires hands-on ML model training (supervised/unsupervised), scikit-learn, MLOps practices (model registry, CI/CD for models, drift detection), and feature engineering — none of which candidate has demonstrated. The 'Scrum/Kanban' requirement is a weak red flag. To improve, candidate should build and deploy a classical ML model (e.g., demand forecasting with scikit-learn), document the MLOps lifecycle, and add experiment tracking with MLflow. **Strengths:** Python proficiency, SQL and data pipeline experience, LLM pipeline design shows adjacent analytical thinking **Critical Gaps:** No ML model development experience (training, validation, hyperparameter tuning) **Missing Required:** ML Engineer or similar role experience, scikit-learn or similar ML libraries, MLOps CI/CD for models, feature engineering experience

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Similar to Job 2 but even more infrastructure-heavy on ML pipelines. Candidate lacks experience with ML frameworks (PyTorch, TensorFlow, scikit-learn), Kubeflow, MLflow, Spark, feature stores, and cloud ML platforms. The candidate's strength in data pipelines and Go orchestrator is relevant conceptually, but the specific ML tooling is entirely missing. To improve, candidate would need to build an end-to-end ML pipeline with experiment tracking, model registry, and feature store — a significant skill gap to bridge. **Strengths:** Python and data pipeline experience, Production systems mindset, Git and CI/CD practices **Critical Gaps:** No ML model operationalization experience, No ML framework experience **Missing Required:** ML pipeline development, ML frameworks (scikit-learn, TensorFlow, PyTorch), Kubeflow or similar, MLflow or similar, Spark

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Candidate has relevant experience with LLMs (Claude/Gemini/OpenAI APIs), RAG concepts, prompt engineering (PID-inspired tuning), and system architecture. The role requires deep hands-on experience with multi-agent orchestration (CrewAI/LangChain), Deep Learning frameworks (PyTorch/TensorFlow), vector databases, MLOps, cloud architecture certifications, and team management of 8 people. Candidate lists LangChain in skills but hasn't demonstrated multi-agent production systems. The management requirement is partially met (leads operations, runs governance meetings). To improve, candidate should build a multi-agent system with LangChain/CrewAI, demonstrate guardrails implementation, and obtain a cloud certification. **Strengths:** LLM API experience (Claude, Gemini, OpenAI), Prompt Engineering with measurable results, RAG and embedding model knowledge **Missing Required:** Proven Architect-level role experience, Deep Learning hands-on, Cloud AI/ML certifications, Multi-agent production experience

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Candidate is significantly overqualified for this 'Assistente' (Assistant) level role. The JD requires Python (candidate has it), ChatGPT/AI tools (candidate uses Claude/Gemini/OpenAI extensively), process mapping and automation (candidate's core strength), and basic dashboard creation (candidate has data visualization and reporting experience). Missing Power BI specifically, but has advanced Excel/VBA and dashboard experience. The cap at 70 applies because Power BI is listed as required. However, the role is entry-level and the candidate's seniority may actually work against them — recruiters may see overqualification as a flight risk. To improve the resume for this role, candidate should de-emphasize architecture and emphasize hands-on Python scripting and process automation. **Strengths:** Python automation experience, AI tools proficiency (ChatGPT equivalent), Process mapping and data cleanup expertise **Missing Required:** Power BI

[ANALYSIS_REPORT] **MEDIUM** [Opencode: opus-4-6] Candidate has strong alignment on several dimensions: n8n (listed in skills), Python and Node.js/TypeScript, LangChain for AI agents, OAuth2/authentication, exception handling and resilience patterns (demonstrated in orchestrator with watchdog timers, state persistence), and production automation experience. However, the critical requirement is RPA suite experience (UiPath/Automation Anywhere/Power Automate/Blue Prism) which candidate completely lacks. Banking/financial automation experience is partially met through crypto trading and financial analysis background. Missing specific RPA tooling, auditable automations, and messaging queue experience. To improve, candidate should highlight n8n and LangChain agent experience more prominently, and emphasize resilience patterns from the Go orchestrator. **Strengths:** n8n experience (directly listed in JD), LangChain for AI agents in production, OAuth2 and security practices, Python and Node.js proficiency **Missing Required:** RPA suite experience (explicitly required), Banking/financial routine automation

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] Candidate has genuine automation leadership and business-to-technical translation, plus some GenAI exposure, which maps to parts of the role. However the JD is explicitly centered on RPA ecosystems (UiPath/Automation Anywhere/Blue Prism) and enterprise intelligent automation toolchains (process/task mining), which are not present. Improve by adding direct RPA platform delivery experience and naming the specific tools and deployments. **Strengths:** automation-first operations design, team/ownership signals (founder/head role), data quality + governance instincts **Missing Required:** enterprise RPA tool experience (UiPath/Automation Anywhere/Blue Prism), Intelligent Automation platform ownership in enterprise stack

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This role requires Java 11/17/21 with Spring Boot 3, Kafka messaging, Redis, MongoDB, and SQL Server — candidate has none of these. The candidate's stack (Go, Python, PostgreSQL) is in a completely different ecosystem. Additionally, the role is hybrid in Recife or Uberlandia, and candidate is in Sao Paulo. The Pix (Brazilian instant payment) domain requires specific financial systems knowledge. Hexagonal architecture knowledge is also required. This is a fundamental technology mismatch. No modifications to the resume could bridge this gap. **Strengths:** CI/CD knowledge, Microservices conceptual understanding **Critical Gaps:** Java ecosystem — entirely different technology stack **Missing Required:** Java development experience, Spring Boot 3, Kafka/messaging, Redis, MongoDB

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.2] Candidate matches AI-assisted, multi-file system ownership and architecture oversight, but lacks explicit "Cursor as primary" evidence and conventional 5+ years professional software development framing. ATS will treat this as a tool/workflow credentialed role and downgrade without exact matches. Improve by explicitly stating Cursor usage (if true), showing a portfolio example of AI-driven implementation across a repo with tests and reviews, and clarifying software engineering years (2020–present). **Strengths:** systems architecture + delivery, LLM integration and prompt pipeline design, disciplined human-in-the-loop review **Missing Required:** 5+ years professional software development experience (clearly evidenced), deep experience using Cursor as primary development environment

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Hard filter: the role requires a Doctoral degree (Titulo de Doutor) as an obligatory requirement. Candidate has a Bachelor's degree in Mechatronics Engineering. This is an academic fellowship (Bolsista) through the Inova Talentos program, specifically designed for PhD holders. No resume modifications can overcome this requirement. The role's AI/chatbot focus would otherwise have some alignment with candidate's LLM experience. **Strengths:** Python proficiency, AI/LLM experience, Automation experience **Critical Gaps:** Doctoral degree required — candidate has Bachelor's only **Missing Required:** PhD/Doctoral degree

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] Candidate has SQL/Python and strong data quality instincts, but the role is specifically Databricks + PySpark + Delta Lake + enterprise data modeling. ATS will treat missing Databricks/PySpark as major required gaps. Improve by building a small Databricks/PySpark pipeline project (Delta Lake, window functions, orchestration) and listing it prominently. **Strengths:** SQL fundamentals (general), data remediation/validation mindset, systems/orchestration experience (adjacent) **Critical Gaps:** Databricks/PySpark production delivery (core domain) **Missing Required:** Databricks, PySpark, Delta Lake fundamentals, enterprise data modeling (star schema)

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] This role is .NET/C# + SQL Server in an agile product/support environment; the resume does not show C#/.NET production experience. ATS will heavily down-rank without the core language/framework match. Improve only by adding real .NET/C# project work (APIs, OOP, SQL Server) and explicitly listing it as recent experience. **Strengths:** SQL (general), Git/CI/CD (general), production troubleshooting mindset **Critical Gaps:** Primary stack mismatch (.NET/C# role) **Missing Required:** .NET, C#, 4+ years software development/support in this stack

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] The role requires solid experience in C/C++ for embedded systems. Candidate has zero C/C++ experience — entire stack is Go, Python, JavaScript, SQL. While the Mechatronics Engineering degree is directly relevant to the field (and even listed as qualifying), the hard skill requirement of C/C++ embedded development is completely missing. Automotive domain tools (Vector CANalyser, CANoe) are also absent. No resume modifications can bridge zero C/C++ experience. **Strengths:** Mechatronics Engineering degree matches education requirement, English proficiency **Critical Gaps:** C/C++ embedded systems — entirely different technology domain **Missing Required:** C/C++ solid experience, Embedded systems projects

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.2] Same pattern as other Cursor agentic roles: Candidate matches AI-assisted systems delivery and architectural oversight, but lacks explicit Cursor-first evidence and conventional software engineering title/tenure signaling. ATS rewards exact-tool alignment here. Improve by explicitly listing Cursor usage and showing a portfolio example where AI generated most implementation with Candidate-led review, tests, and refactors. **Strengths:** end-to-end system shipping, LLM integration across providers, strong review/security discipline **Missing Required:** 5+ years professional software development experience (clearly evidenced), deep experience using Cursor as primary development environment

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.2] Candidate matches Go, systems architecture, and operational mindset, but the JD signals high-scale distributed systems ownership (SLIs/SLOs, observability, reliability) at very high traffic, which is not explicitly demonstrated. ATS will score general backend skills, yet may downgrade for lack of "high-scale" keywords and conventional backend titles. Improve by adding concrete reliability/perf work (load, latency, error budgets, monitoring) and any evidence of concurrency/networking work in Go. **Strengths:** Golang production systems, automation + operational ownership, clear technical communication **Missing Required:** high-scale backend/distributed systems experience (clearly evidenced)

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] Palantir Foundry experience is explicitly mandatory and not present; ATS will treat this as a hard reject regardless of adjacent data/automation strengths. The resume shows data systems skill, but not the Foundry platform, Pipeline Builder, Ontology, or Quiver. Improve only by gaining real Foundry project experience and listing it clearly. **Strengths:** data remediation mindset, automation and integration skills, analytical/problem-solving **Critical Gaps:** Mandatory Palantir Foundry experience **Missing Required:** Palantir Foundry development (mandatory), Foundry Pipeline Builder (mandatory)

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] ATS will see strong general API/integration capability (Go/Node, SQL, middleware-style work) but not the core SAP PI/PO + EDI/SSIS stack this role is built around. SAP PI/PO is treated as deep, specific expertise here, so the resume reads like “adjacent” rather than “hands-on.” To improve: add any explicit SAP integration exposure (even project-based), name concrete integration patterns (IDoc/EDI flows, retries, idempotency), and show tooling parity (API mgmt platform, Lambda, SSIS equivalents). **Strengths:** API development/integrations, SQL + data remediation mindset, Python/automation capability **Critical Gaps:** SAP PI/PO hands-on integration experience **Missing Required:** SAP Process Orchestration (SAP PI/PO)

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This is a Senior Java Backend Engineer role requiring Java 17+, Spring Boot, Kafka, RabbitMQ, MongoDB, Redis, Docker/Kubernetes, and Jenkins/GitLab CI. Candidate has no Java experience whatsoever. The role also requires Scrum/Kanban methodology experience and technical leadership of development teams. While candidate has Docker, Git, CI/CD, and system architecture knowledge, the Java ecosystem requirement is a complete blocker. This is fundamentally a Java role. **Strengths:** Docker experience, CI/CD knowledge, System architecture thinking **Critical Gaps:** Java ecosystem — entirely different technology stack **Missing Required:** Java proficiency, Spring Boot, Kafka/RabbitMQ messaging, MongoDB/Redis

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This role requires ServiceNow platform expertise and ServiceNow certifications (CSA, CIS, CAD). Candidate has zero ServiceNow experience. While candidate has JavaScript, Python, and database skills that are tangentially relevant, ServiceNow is a highly specialized platform requiring specific certifications that are listed as mandatory requirements. The role is also at EY (Big 4 consulting firm), which is a strong culture red flag. No resume modifications can bridge zero ServiceNow experience plus missing required certifications. **Strengths:** JavaScript and Python proficiency, Database experience **Critical Gaps:** ServiceNow platform expertise — completely different specialization **Missing Required:** ServiceNow experience, ServiceNow certifications (CSA, CIS, CAD), Angular