Senior Machine Learning Engineer

• Own the full model lifecycle — feature engineering, training, validation, registration, and deployment
• Monitor models in production and lead retraining when performance degradation or drift occurs
• Maintain and evolve CI/CD pipelines that promote models and pipelines across development, staging, and production environments
• Automate and productionize model and agent evaluation pipelines in support of Data Science
• Build and maintain reusable internal metrics packages for standardized experiments
• Operate the infrastructure for fine-tuning language models
• Administer the AI Gateway, including model routing, rate limits, and cost control and attribution
• Ensure feature, experiment, and model governance is aligned with Afya policies
• Serve as the technical bridge between Data Science, AI Engineering, Data Engineering, and SRE
• Apply scalability, security, and cost-efficiency practices to ML/AI infrastructure

• Databricks as the primary platform, serving as a technical authority: Model Serving, Unity Catalog, and Asset Bundles, with MLflow for model registration, versioning, and evaluation
• Equivalent experience with SageMaker, Vertex AI, or Azure ML is also valued, along with a willingness to deepen expertise in Databricks
• Advanced Python, SQL, and Apache Spark / PySpark, applied to production Machine Learning pipelines (scikit-learn, XGBoost)
• CI/CD and infrastructure as code for promoting models and pipelines across development, staging, and production: GitHub Actions, Terraform (or equivalent IaC), cloud platforms (Azure, AWS, or GCP), and secrets management
• Production model operations: version promotion and rollback, performance, cost, and drift monitoring, and response to endpoint incidents
• Statistical interpretation of evaluation results to determine version promotion or rollback, with governance of experiments, model versions, and inference data
• Exposure to at least one LLMOps area: AI Gateway, evaluation (evals) pipeline automation and reusable internal metrics packages, or LLM fine-tuning infrastructure
• Senior technical leadership without people management, including architecture definition, negotiation of technical contracts, and mentoring mid-level engineers
• Completed higher education or a technology degree in Computer Science, Systems Analysis and Development, Engineering, Information Systems, Statistics, Mathematics, or a related field
• Advanced technical English for reading documentation, release notes, and papers
• Established experience in Machine Learning Engineering / MLOps, with demonstrated experience supporting models in production: automated training, validation, promotion, deployment, monitoring, and retraining

• Meal / food allowance
• Flexible working hours and arrangements (for remote positions)
• Transportation allowance (for hybrid or on-site positions)
• Profit-sharing bonus
• Flexible benefits: flexible allowance via Flash Card for use as preferred
• Gympass / Wellhub
• Psicologia Viva (online platform for consultations with psychologists and nutritionists)
• Health and dental insurance
• Life insurance
• Extended parental leave (up to 6 months for mothers and 20 days for fathers)
• Rede D'Or: support and important health information for mothers and babies through a network of accredited nurses
• Birthday Day Off (one day off to take on your birthday or at any time during your birthday month)
• Platform offering a variety of courses to enhance your knowledge (UCA)
• Language academy (AIA)
• Leadership development program
• Discounts on undergraduate and graduate courses at Afya educational units
• Premium subscription to Afya iClinic and Afya Whitebook (an added benefit for physician professors)