Senior Machine Learning Engineer

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Lead the design, industrialization, and advancement of Machine Learning products at an enterprise level.

• Oversee the architecture, governance, operationalization, and support of ML solutions in production.

• Manage the complete solution design and development process, including monitoring, documentation, and continuous enhancement.

• Spearhead MLOps initiatives encompassing model training, deployment, model serving, monitoring, and lifecycle governance.

• Create and manage ETL/ELT pipelines, DAGs, as well as data and Machine Learning workflows using PySpark.

• Design and oversee enterprise Feature Stores, ensuring consistency, versioning, and lineage between training and inference.

• Develop, validate, and operationalize Machine Learning models for various analytical applications.

• Implement model versioning strategies, Champion/Challenger methodologies, rollouts, model promotion, and manage the Model Registry.

• Ensure observability, quality, traceability, reproducibility, and governance across data, features, pipelines, and models.

• Design and execute CI/CD processes and Infrastructure as Code (IaC) for Machine Learning platforms.

• Establish architectural standards, engineering best practices, and MLOps guidelines.

• Perform technical code reviews and assist Data Scientists in the industrialization of ML solutions.

• Maintain comprehensive technical, architectural, and operational documentation.

• Lead new initiatives and continuously enhance the organization’s data and MLOps platform.

⛳️ Requirements

• Advanced experience with Databricks, including MLflow, Unity Catalog, Delta Lake, Databricks Workflows, Model Registry, Model Serving, and Databricks Asset Bundles (DABs).

• Strong background in developing, operationalizing, and monitoring Machine Learning models in production environments.

• Experience with Feature Engineering, hyperparameter optimization, model evaluation, and both supervised and unsupervised learning algorithms.

• Familiarity with enterprise Feature Stores, including feature versioning and point-in-time lookups.

• Knowledge of Data Drift, Concept Drift, Performance Drift, and the observability of data and ML pipelines.

• Proven experience in building CI/CD pipelines, managing DEV, QA, and PROD environments, and implementing Infrastructure as Code.

• Experience with automated testing for both data and Machine Learning pipelines.

• Proficiency in distributed processing and optimizing Spark workloads.

• Strong command of Python, PySpark, SQL, MLflow, Spark MLlib, and key Machine Learning ecosystem libraries.

• Understanding of secure credential and secrets management, such as Service Principals, Key Vault, or similar solutions.

• Experience with Azure DevOps or similar tools.

• Intermediate proficiency in English.

• Ability to collaborate with global teams and produce technical documentation in English.

• Databricks Certified Machine Learning Professional is highly preferred.

• Databricks Certified Data Engineer Professional.

• Experience with GenAI, LLMOps, and RAG architectures.

🏝️ Benefits

• Health and dental insurance.

• Meal and food allowance.

• Childcare assistance.

• Extended paternity leave.

• Partnerships with gyms and health and wellness professionals via Wellhub (Gympass) TotalPass.

• Profit Sharing and Results Participation (PLR).

• Life insurance.

• Continuous learning platform (CI&T University).

• Discount club.

• Free online platform focused on physical, mental, and overall well-being.

• Pregnancy and responsible parenting courses.

• Partnerships with online learning platforms.

• Language learning platform.

• Health and Well-being team, inclusion specialists, and affinity groups.

• Support and accommodations for individuals with disabilities during the selection process.