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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in managing the full model lifecycle, including feature engineering, training, validation, and deployment, while ensuring governance and performance monitoring of machine learning models in production. Proficient in CI/CD practices and infrastructure as code to automate and optimize model operations across various cloud platforms.
Highest-signal resume keywords
Databricks ExpertiseAdvanced Python ProgrammingCI/CD and Infrastructure as CodeMachine Learning Engineering / MLOpsStatistical Interpretation of Evaluation Results
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Feature EngineeringModel TrainingModel ValidationModel DeploymentApache Spark / PySparkSQLMachine Learning PipelinesStatistical AnalysisModel GovernancePerformance Monitoring
Soft Skills
Technical LeadershipMentoringNegotiation
Tools & Technologies
DatabricksMLflowGitHub ActionsTerraformAzureAWSGCPAI GatewayXGBoostScikit-learn
Industry Keywords
MLOpsModel ServingLLMOpsCost ControlData ScienceInfrastructure ManagementExperiment GovernanceModel Drift MonitoringVersion ControlCloud Platforms
Tech Stack
Tools & technologiesApacheAWSAzureCloudGoogle Cloud PlatformPySparkPythonScikit-LearnSparkSQLTerraformUnity
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- 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
Benefits
Comp & perks- 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)