Anti-ATS Evaluatorv1.3

[ANALYSIS_REPORT] **LOW** [Opencode: opencode:kimi-k2.5-free] Mechatronics degree relevant but not explicitly recognized. Missing Node.js, Azure, Databricks, Headless Commerce. 'Principal Architect' title requires formal architecture role history. CI&T is large consulting firm (RED FLAG). Fluent English present. **Strengths:** Mechatronics Engineering, Systems architecture, Python, Microservices concepts, Fluent English, Portuguese native for Brazil role **Critical Gaps:** Azure cloud platform, Databricks, Headless Commerce architecture **Missing Required:** Solution/Enterprise Architect experience (title), Node.js, Azure ecosystem

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT4.1] ATS detects strong Python, SQL, and data pipeline experience, but lacks direct Cognite Data Fusion, Azure Data Factory, and Databricks expertise. Candidate demonstrates advanced automation, API integration, and database skills, but misses specific platform requirements. No critical gaps, but missing_required includes Azure and Cognite-specific tools. **Strengths:** Python automation, SQL database management, REST API integration **Missing Required:** Azure Data Factory, Cognite Data Fusion

[ANALYSIS_REPORT] **LOW** [3-Flash] The role requires a Senior Architect with deep expertise in C# and .NET. The candidate's architectural experience is centered on Go and Python, making this a fundamental technical mismatch. Additionally, the role is affirmative for women, and the candidate does not meet this demographic requirement. **Strengths:** Architectural design, Docker/CI/CD **Critical Gaps:** Tech stack mismatch, Demographic requirement (Affirmative for Women) **Missing Required:** C#, .NET

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] Strong match for Python, OOP, Docker, and SQL. The candidate's experience with AWS foundations and building scalable systems aligns well with the 'Pleno' requirements. The missing specific AWS services (SQS, ECS) are inferred but not explicitly detailed in the resume as primary tools. **Strengths:** Python OOP, Docker/CI/CD, Microservices architecture **Critical Gaps:** Work shift mismatch (03h-12h) **Missing Required:** AWS intermediário (specific services)

[ANALYSIS_REPORT] **LOW** [3-Flash] Candidate has foundational Cloud/DevOps skills (Docker, AWS, CI/CD) but lacks the required 'Pleno' mastery of IaC (Terraform/Ansible) and multi-cloud (Azure) management. The JD's focus on compliance (ISO 27001) and specific migration experience is not reflected in the candidate's profile. **Strengths:** AWS foundations, Docker/Containers, Scripting/Automation **Critical Gaps:** Missing Terraform/IaC core requirement **Missing Required:** Terraform/Ansible, Azure experience, Cloud Certifications

[ANALYSIS_REPORT] **TOP** [3-Flash] High semantic match for 'Python', 'LLM orchestration', and 'Agents'. The candidate's recent work on an AI-driven market intelligence platform with custom scoring and agent-like logic is a direct parallel to the JD's requirement for building LLM agents and serverless backends. **Strengths:** Custom LLM Orchestration, Python Backend, Problem Solving Mindset **Missing Required:** Experience with specific AI frameworks like LangChain/LlamaIndex (uses raw APIs instead)

[ANALYSIS_REPORT] **LOW** [3-Flash] Staff level roles require deep expertise in high-scale data streaming (Kafka, Flink, Spark) and distributed systems, which are gaps in the candidate's profile. While strong in Python/SQL and basic containers, the candidate lacks the 'Staff' level experience with complex Data Platforms and MPP warehouses. **Strengths:** Python/SQL, Systems thinking, Data Architecture concepts **Critical Gaps:** Missing streaming/distributed systems expertise **Missing Required:** Spark/Kafka, Staff-level leadership in tech

[ANALYSIS_REPORT] **LOW** [Copilot: GPT4.1] ATS flags critical gap: no Workfront or Adobe ecosystem experience, and lacks required developer certification. Candidate's automation and systems skills are not recognized by ATS for project management system roles. Hard filter applies due to missing Workfront-specific expertise and certification. **Strengths:** Automation, Systems architecture, Client-facing project delivery **Critical Gaps:** Workfront ecosystem, Developer certification **Missing Required:** Workfront, Workfront Fusion, Developer certification

[ANALYSIS_REPORT] **LOW** [Copilot: GPT4.1] ATS flags critical gap: no Airflow experience, no insurance domain background, and no 'Software Engineer' title. Candidate's Python and automation skills are strong, but ATS does not recognize them for Airflow-specific requirements. **Strengths:** Python automation, CI/CD concepts, Data pipeline design **Critical Gaps:** Airflow, Insurance domain **Missing Required:** Airflow, Insurance experience

[ANALYSIS_REPORT] **LOW** [3-Flash] The role is specifically for a Scala Data Engineer. The candidate has no experience with Scala, Spark, or the AWS Big Data stack (EMR, Glue, Lake Formation). Python skills are secondary to the Scala/Big Data requirement. **Strengths:** Python, Data Pipelines logic **Critical Gaps:** No Scala/Java experience **Missing Required:** Scala, Spark

[ANALYSIS_REPORT] **LOW** [Copilot: GPT4.1] ATS flags critical gap: no AWS RDS/Aurora experience, no advanced SQL performance tuning, and no 'Data Architect' or equivalent title. Candidate's systems and database skills are strong, but ATS does not recognize them for deep database optimization roles. **Strengths:** Systems architecture, Database management, Automation **Critical Gaps:** AWS RDS/Aurora, Advanced SQL optimization **Missing Required:** AWS RDS, Aurora

[ANALYSIS_REPORT] **LOW** [Copilot: GPT4.1] ATS flags critical gap: no Microsoft 365 migration experience, no Quest ODM T3 familiarity, and no direct IT support background. Candidate's automation and systems skills are not recognized for M365 migration roles. **Strengths:** Automation, Client-facing project delivery, Documentation **Critical Gaps:** Microsoft 365 migration, Quest ODM T3 **Missing Required:** Microsoft 365 migration, Quest ODM T3

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] Strong match on Python and LLM/RAG concepts. However, the requirement for 5-6 years of software development 'focused on AI' and experience with heavy ML libraries (TensorFlow/PyTorch) is a gap, as the candidate's AI work is more recent and focused on API integration/orchestration rather than model training. **Strengths:** LLM/RAG implementation, Python proficiency, Cloud/AWS **Critical Gaps:** Missing 5-6 years specifically in AI engineering **Missing Required:** TensorFlow/PyTorch

[ANALYSIS_REPORT] **TOP** [Copilot: GPT4.1] ATS detects strong Kubernetes, Docker, Linux, GPU cluster, and cloud-native experience, with direct hands-on orchestration and monitoring. Candidate demonstrates deep systems architecture, containerization, and AI infrastructure skills, matching most requirements. Minor gaps in specific cluster management tools (Ray, Slurm, KubeSphere) and advanced GPU programming (CUDA/NCCL), but not critical. **Strengths:** Kubernetes orchestration, GPU cluster management, AI infrastructure

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT4.1] ATS detects strong Python, SQL, CI/CD, and automation skills, but lacks direct Databricks, MLflow, and Spark experience. Candidate demonstrates advanced ML pipeline and production support skills, but misses Databricks-specific requirements. No critical gaps, but missing_required includes Databricks and MLflow. **Strengths:** Python automation, CI/CD pipeline, Production ML systems **Missing Required:** Databricks, MLflow

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] The candidate has excellent GenAI and RAG architectural knowledge, but this is a Tech Lead role focused on Data Infrastructure (Databricks, Spark, MLOps at scale). The candidate lacks experience with MLOps tools like MLflow and the enterprise data stack (Databricks) required for this level of leadership. **Strengths:** GenAI/LLM architecture, Python/SQL, Systems thinking **Critical Gaps:** No experience with Databricks/MLflow, Tech Lead seniority gap **Missing Required:** Databricks, Spark, MLflow

[ANALYSIS_REPORT] **HIGH** [3-Flash] Good match for a candidate who can act 'from zero' and build architecture in an unstructured environment. The candidate's background as a founder and independent engineer fits the 'protagonist' and 'construction' profile perfectly. Missing specialized ML research/training background is a slight gap for a 'Machine Learning Engineer' title. **Strengths:** Hands-on construction, Python for automation, Protagonist profile **Missing Required:** Heavy predictive modeling experience

[ANALYSIS_REPORT] **TOP** [3-Flash] Strongest niche match. The JD looks for an 'Engineer mindset', 'Product focus', and interest in 'Industrial/IoT' contexts. The candidate's Mechatronics degree, Supply Chain (TOTVS/ERP) background, and recent AI/Python work create a unique overlap that outweighs the lack of a traditional SE title. **Strengths:** Mechatronics + AI overlap, Industrial context experience, Python/APIs **Missing Required:** CV experience (though Mechatronics is a strong proxy)

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT4.1] ATS detects strong Python, SQL, CI/CD, and automation skills, but lacks direct Databricks, MLflow, and Spark experience. Candidate demonstrates advanced ML pipeline and production support skills, but misses Databricks-specific requirements. No critical gaps, but missing_required includes Databricks and MLflow. **Strengths:** Python automation, CI/CD pipeline, Production ML systems **Missing Required:** Databricks, MLflow

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] Matches on Python and GenAI, but 'MLE Especialista' implies a depth in MLOps and Model Lifecycle Management (LCM) in high-scale environments that the candidate hasn't documented. The candidate's work is more 'building the system' than 'operationalizing high-scale models'. **Strengths:** Python, AWS Cloud, GenAI application **Critical Gaps:** Missing deep MLOps/MLE specialization **Missing Required:** LCM experience

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT4.1] ATS detects strong Python, SQL, CI/CD, and automation skills, but lacks direct Databricks, MLflow, and Spark experience. Candidate demonstrates advanced ML pipeline and production support skills, but misses Databricks-specific requirements. No critical gaps, but missing_required includes Databricks and MLflow. **Strengths:** Python automation, CI/CD pipeline, Production ML systems **Missing Required:** Databricks, MLflow

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT4.1] ATS detects strong Python, SQL, CI/CD, and automation skills, but lacks direct Databricks, MLflow, and Spark experience. Candidate demonstrates advanced ML pipeline and production support skills, but misses Databricks-specific requirements. No critical gaps, but missing_required includes Databricks and MLflow. **Strengths:** Python automation, CI/CD pipeline, Production ML systems **Missing Required:** Databricks, MLflow

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT4.1] ATS detects strong Python, SQL, CI/CD, and automation skills, but lacks direct Databricks, MLflow, and Spark experience. Candidate demonstrates advanced ML pipeline and production support skills, but misses Databricks-specific requirements. No critical gaps, but missing_required includes Databricks and MLflow. **Strengths:** Python automation, CI/CD pipeline, Production ML systems **Missing Required:** Databricks, MLflow

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT4.1] ATS detects strong Python, SQL, CI/CD, and automation skills, but lacks direct Databricks, MLflow, and Spark experience. Candidate demonstrates advanced ML pipeline and production support skills, but misses Databricks-specific requirements. No critical gaps, but missing_required includes Databricks and MLflow. **Strengths:** Python automation, CI/CD pipeline, Production ML systems **Missing Required:** Databricks, MLflow

[ANALYSIS_REPORT] **MEDIUM** [Opencode: opencode:haiku-4-5] Candidate demonstrates strong foundational skills in Python, SQL, APIs, and data pipelines from e-commerce and market intelligence work. However, lacks demonstrable experience with Elasticsearch, semantic search implementation, learning-to-rank models, and formal Information Retrieval training. ATS recognizes Python, SQL, A/B testing framework, and product collaboration, but will penalize missing Elasticsearch and IR-specific expertise. **Strengths:** Python proficiency, SQL mastery, End-to-end product thinking, A/B testing and experimentation mindset, Data pipeline architecture **Missing Required:** Elasticsearch hands-on experience, IR and relevance ranking experience