Anti-ATS Evaluatorv1.3

[ANALYSIS_REPORT] **LOW** [Opencode: google/gemini-3.1-pro-preview] Candidate has excellent general automation experience (n8n, Python, Puppeteer), but the role rigidly requires enterprise UiPath and C#/.NET, which are completely absent. **Strengths:** General RPA concepts, API integration **Critical Gaps:** UiPath, C# **Missing Required:** 3+ years UiPath, C#/.NET

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Software Architect role requiring deep .NET/C# expertise, Kubernetes (AKS/EKS), Kafka/RabbitMQ/Azure Event Hub, CQRS, event-driven architecture, and performance monitoring (Grafana, Prometheus, K6). The candidate has PostgreSQL, Docker, and systems architecture thinking, but zero .NET/C# experience, no Kubernetes, no Kafka, and no enterprise event-driven architecture experience. The core tech stack is entirely .NET-based. **Strengths:** Systems architecture thinking, Docker experience, PostgreSQL experience, Infrastructure optimization mindset **Critical Gaps:** .NET / C# (entire role is built on this stack), Kubernetes **Missing Required:** .NET/C#, Kubernetes (AKS/EKS), Kafka/RabbitMQ messaging, CQRS pattern, Performance testing tools (K6, Artillery)

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] AWS-focused Data Platform/DevOps role requiring Terraform IaC, deep IAM governance, ECS/EKS containers, Kafka/Kinesis streaming, Glue Catalog, and GitHub Actions CI/CD. The candidate has basic AWS knowledge, Docker, Git, CI/CD concepts, and Linux, but lacks Terraform, deep IAM expertise, ECS/EKS, Kafka/Kinesis, and Glue Catalog experience. The 'basic AWS' on resume vs. 'deep AWS expertise required' is a significant gap. **Strengths:** Docker experience, Linux proficiency, CI/CD concepts, Basic AWS knowledge **Critical Gaps:** Terraform / IaC (core requirement with no candidate experience) **Missing Required:** Terraform, AWS IAM governance, ECS or EKS container orchestration, Kafka/Kinesis streaming, GitHub Actions

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] The resume shows strong automation, Linux, SQL, and CI/CD experience but lacks direct hands-on work with the specific AWS data stack, infrastructure-as-code tools, and distributed data platforms this Senior Data DevOps role emphasizes. Several required technologies (Redshift/Glue/EMR/Athena, Terraform/CloudFormation, Spark/Airflow, Kafka/NiFi, detailed cloud networking) are missing or only covered at a very general level (AWS fundamentals). Titles and narrative emphasize systems automation and business-technical bridging rather than explicit Data Engineering/DevOps in AWS, which reduces semantic ATS alignment despite solid underlying skills. **Strengths:** Strong Linux-based systems engineering and automation experience, including running production workloads on constrained servers, Solid SQL/PostgreSQL and data remediation background with real-world ERP and reporting reliability projects, Demonstrated CI/CD, orchestration, and system design skills through the job intelligence platform and e-commerce automation **Missing Required:** Hands-on experience with AWS data services such as Redshift, Glue, EMR, and Athena, Infrastructure as Code with Terraform, AWS CloudFormation, or Ansible, Practical experience with Apache Spark for distributed data processing, Practical experience with Apache Airflow for workflow orchestration, Proven skills installing and managing platforms such as Apache Kafka and NiFi, Documented cloud networking expertise with TCP, UDP, ICMP, DNS, and NAT in AWS contexts, Clear 3+ years in explicitly titled Data Engineering/DevOps or closely named cloud infrastructure roles

[ANALYSIS_REPORT] **MEDIUM** [Opencode: opus-4-6] Good alignment on Python backend development, LLM/AI agent experience, RAG implementation, and API REST integration. The candidate has built AI pipelines, used Claude/OpenAI/Gemini APIs, implemented RAG with vector search, and has production Python code. Key gaps: MongoDB is required (candidate uses PostgreSQL/SQLite), no demonstrated OOP design patterns in Python, no pytest/unittest mentioned explicitly, and 'sólida experiência com Python backend' may not match candidate's recent Python usage which is more scripting/pipeline than enterprise backend. The Agno Framework differential is unmet. **Strengths:** RAG implementation experience (direct required match), LLM/AI agent experience (direct required match), Python development and API integration, API REST experience **Missing Required:** MongoDB (explicit required skill), Formal OOP design patterns demonstration, Automated testing (pytest/unittest)

[ANALYSIS_REPORT] **TOP** [Opencode: opus-4-6] Strong alignment on the core requirements: LLM experience (OpenAI, Anthropic), LangChain/LangGraph, Python, RAG with vector databases, API integration, and system architecture. The candidate has built production AI pipelines, used Claude/Gemini/OpenAI APIs, implemented prompt engineering with statistical validation, and designed system architecture. The candidate's 'builder mindset' and ability to translate tech into business solutions directly match the behavioral profile. Gaps: no explicit LangGraph experience (has LangChain), cloud deploy experience is basic (AWS fundamentals vs. required AWS/GCP/Azure deploy), and the role is presential in São José/SC while candidate is in São Paulo. **Strengths:** LLM experience with OpenAI and Anthropic (direct match), LangChain experience (direct match), Python proficiency, RAG and vector database experience, System architecture and API integration, Builder mindset matches behavioral profile perfectly **Missing Required:** Cloud deployment experience at depth (basic AWS vs. required cloud deploy)

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT4.1] The candidate demonstrates strong experience with LLMs, prompt engineering, RAG, and production deployment, directly matching most core requirements. However, there is no explicit experience with LangChain/LangGraph, and the role is hybrid in Lajeado/RS, which may be a geographic blocker. Some nice-to-have skills (AWS, technical leadership in larger teams) are not fully evidenced. **Strengths:** LLM production deployment, Prompt engineering, System architecture for AI **Missing Required:** Hybrid presence in Lajeado/RS (candidate is Sao Paulo-based)

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT4.1] The candidate matches nearly all core requirements: LLMs, RAG, vector DBs, MLOps, and production deployment, with strong evidence of hands-on delivery and systems architecture. There is no explicit mention of PydanticAI, LlamaIndex, or deep fine-tuning, but adjacent skills and project complexity compensate. All hard requirements (degree, English fluency, Python, vector DBs) are met or strongly inferred. **Strengths:** LLM/RAG production systems, Vector DB familiarity, MLOps and observability

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5-mini] Reasonable semantic match on Python, data pipelines, relational DBs and production orchestration but missing explicit Airflow and Kafka experience required for data-platform roles. Projects demonstrate applied data ingestion and pipeline ownership but required tooling gaps reduce ATS confidence. Missing required items trigger a conservative cap in scoring. **Strengths:** Python data pipelines, PostgreSQL/SQL expertise, Production orchestration and scaling on constrained infra **Missing Required:** Experience developing Airflow DAGs, Experience with Kafka or similar

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] ATS finds some keyword matches: Python, SQL, PostgreSQL, financial market experience (nice-to-have). However, the JD requires deep expertise in Databricks, Apache Spark/PySpark, MongoDB Aggregation Framework, Delta Lake, and Kafka — all of which are critical gaps with no inference possible from the candidate's demonstrated work. The role demands Big Data/distributed processing experience at scale which the candidate lacks entirely. **Strengths:** Financial market experience (nice-to-have match), Python familiarity, SQL/PostgreSQL experience **Critical Gaps:** Databricks platform mastery, Apache Spark/PySpark development, MongoDB advanced (Aggregation Framework, sharding, replication) **Missing Required:** Databricks, Apache Spark/PySpark, MongoDB advanced, Big Data project experience

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5-mini] Candidate has strong systems and production experience (Docker, Linux, monitoring mindset) but lacks the required deep, hands-on Datadog SME experience demanded by the JD. Azure/Kubernetes/Terraform familiarity is partial (Docker and AWS fundamentals present), so ATS penalizes absence of Datadog and Terraform/K8s depth. Overall fit is adjacent but misses the single most critical technology. **Strengths:** Production observability mindset inferred, Docker/Linux/Go experience, Incident handling and platform thinking **Missing Required:** 3+ years hands-on Datadog in production

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5-mini] Good semantic overlap: candidate shows platform and automation design, Go/Python skills, observability & CI/CD familiarity, and Docker experience. Missing explicit multi-region cloud depth (only AWS fundamentals) and limited evidence of IaC (Terraform/CloudFormation) and managed Kubernetes at scale reduces ATS confidence but not fatally. Thoughtworks often values diverse backgrounds which helps human review despite ATS deductions. **Strengths:** Systems architecture and platform design, Go + production webserver/orchestrator, Observability and CI/CD familiarity

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5-mini] Strong overlap on container platforms, microservices, presentation and customer-facing architecture skills; however this Red Hat Sales Solution Architect role expects sales/partner-facing architecture experience and specific OpenShift/Kubernetes depth that are only partially present. ATS likes technical translation and cloud familiarity but flags missing explicit OpenShift and sales/partner-track experience. **Strengths:** Kubernetes/containers familiarity (Docker), Excellent technical translation and architecture skills, SSO/OAuth and integration experience

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5-mini] Good platform-engineer fit on developer experience, Terraform-ish IaC familiarity implied through infrastructure work and CI/CD; explicit 6+ years Terraform + AWS ECS requirements are only partially demonstrated (candidate lists 'AWS Fundamentals' and Docker). ATS rewards platform thinking and Terraform-adjacent skills but deducts for missing deep Terraform/AWS ECS specifics. **Strengths:** Platform engineering and automation, Terraform-adjacent experience inferred, Experience building self-service automation and CI/CD

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5-mini] Strong match on data ingestion, pipeline design, Python/SQL skills and production data-pipeline ownership; candidate shows applied ETL/validation and schema work across roles. Lack of explicit streaming framework names (Kafka/Kinesis/Spark) and missing formal CS degree isn't fatal; ATS gives good weight to applied pipeline projects and MITx credential. **Strengths:** Python + ETL/ELT experience, Data ingestion and validation ownership, Observability and automation focus

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] The JD requires deep expertise in React.JS, C# (.NET), and Microsoft SQL Server — the candidate has none of these. This is a fullstack role in a completely different tech stack. The candidate works in Go/Python/JS but not in C#/.NET ecosystem. Additionally, the JD requires certifications in Agile methodologies and AWS SA certification, which the candidate lacks. **Strengths:** JavaScript familiarity (but not React-level), System integration experience, Software architecture thinking **Critical Gaps:** C# (.NET) backend development, React.JS frontend development, Microsoft SQL Server **Missing Required:** React.JS, C# (.NET), Microsoft SQL Server, Agile certifications, AWS SA certification

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5-mini] Duplicate of other Senior Platform roles: candidate has strong platform, Go/Python, Docker, CI/CD and systems design evidence but limited explicit cloud provider seniority (only AWS fundamentals) and Terraform/K8s at scale details. ATS rewards platform architecture and leadership but deducts for missing explicit large-scale cloud/K8s management proof. Human-friendly companies that value systems thinking (like Thoughtworks) increase review likelihood. **Strengths:** Systems architecture, Go & production orchestrator, Observability and CI/CD experience, Cost/performance optimization mindset

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] The candidate has Python experience, SQL knowledge, Git/CI experience, and data pipeline work. The role values automation and data engineering which partially aligns. However, the JD requires deep expertise in data modeling (conceptual, logical, physical), Power BI/Looker Studio visualization, Airflow orchestration, and advanced SQL optimization at scale — areas where the candidate has limited demonstrated depth. The ML/AI differential is a positive signal. **Strengths:** Python experience with practical application, SQL and PostgreSQL knowledge, Git and CI/CD familiarity, AI/ML experience matches differential **Missing Required:** Data visualization tools (Power BI/Looker Studio), Data modeling expertise (conceptual/logical/physical), Pipeline orchestration tools (Airflow)

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This is a .NET/C# fullstack role requiring JavaScript/.NET, SqlServer, Redis, RabbitMQ/Kafka, and AWS services. The candidate has JavaScript and Docker (differentials) but completely lacks C#/.NET, SqlServer, Redis, and messaging systems experience. The core stack mismatch is too severe. **Strengths:** JavaScript experience, Docker knowledge (differential), Git/CI-CD familiarity, PostgreSQL (listed as differential) **Critical Gaps:** .NET/C# development (core requirement), SqlServer, Redis **Missing Required:** .NET/C#, SqlServer, Redis, Messaging systems (RabbitMQ/Kafka)

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5-mini] Same AWS Platform Engineer listing as earlier: good platform thinking, Terraform-adjacent signals, and AWS familiarity but missing explicit long Terraform history and AWS ECS examples. ATS gives credit for platform engineering projects and automation but deducts for required 6+ years of Terraform depth. Candidate can be competitive if Terraform modules and ECS work are made explicit. **Strengths:** Platform engineering mindset, CI/CD and self-service tooling experience, Go/Python and PostgreSQL production experience

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5-mini] This Functional Safety / ISO‑26262 role is domain-specific to automotive FuSa and requires specialized experience not present in the candidate's resume (no ISO‑26262 or FuSa history). ATS treats this as a critical gap because required standards and risk/hazard analysis experience are explicit. Candidate background in mechatronics is related but not sufficient to meet the specialized FuSa requirements. **Strengths:** Mechatronics foundation, Systems thinking, Risk/quality mindset **Critical Gaps:** Domain-specific functional safety experience **Missing Required:** Experience with ISO 26262 standard and FuSa tooling

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5-mini] Same Senior Platform Engineer profile: strong systems design, Go/Python, container and CI/CD evidence but lacking explicit Terraform/K8s at scale and cloud-provider seniority details. Resume demonstrates production-grade platform thinking and IaC-adjacent skills but not all named technologies at the required depth. Overall a solid technical match but not a perfect ATS fit. **Strengths:** Infrastructure and automation architecture, Go-based orchestration and constrained deployment experience, Observability/CI/CD familiarity

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5-mini] Kraken's Core Engineering Rust role requires Rust proficiency and network/asynchronous systems experience; the candidate's resume lists Go/Python and systems expertise but not Rust, which is a blocking technical gap. ATS will heavily penalize absence of Rust and relevant low-level systems language experience despite strong systems thinking. **Strengths:** Distributed systems understanding, Production systems ownership, Debugging across OS/network layers **Critical Gaps:** Rust required but not present **Missing Required:** Proficiency writing network services or async code in Rust

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5-mini] Fullstack Developer (São Paulo) expects Java/Spring + React/Redux + AWS; candidate has JavaScript/Node, Go, and frontend experience is limited to Vanilla JS but not React/Spring/Java. ATS treats missing core stack (Java/Spring/React) as a significant gap despite AWS and AI-tool experience. **Strengths:** Backend APIs and SQL experience, AI-assisted development experience, AWS fundamentals **Missing Required:** Proficiency in Java and Spring, React/Redux front-end experience

[ANALYSIS_REPORT] **TOP** [Copilot: GPT5-mini] Agentic Software Engineer (Cursor) requires AI-first workflows, prompt/context engineering and heavy experience with AI-generated code; candidate has direct, recent LLM pipeline work, RAG, prompt tuning and agent-like tooling and demonstrates proof-of-work. ATS will value the explicit LLM/RAG and AI-dev experience while deducting for lack of explicit Cursor usage (tool-specific). **Strengths:** LLM pipeline design, RAG, prompt engineering, Production AI tooling and validation, Cross-stack implementation and human-in-loop review