Senior MLOps Engineer

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

📋 Description

• Design and manage CI/CD pipelines for machine learning models and LLM/RAG applications, including training, packaging, deployment, and rollback;

• Operationalize the model lifecycle by implementing versioning, maintaining a model registry, utilizing feature stores, orchestrating processes, and ensuring experiment reproducibility;

• Develop and scale serving platforms, both batch and online, with an emphasis on latency, cost efficiency, and availability;

• Establish comprehensive end-to-end observability, including performance monitoring, detection of data/model drift, assessment of data quality, and tracking business metrics;

• Implement governance, security, and cost-control measures for data and AI infrastructures;

• Collaborate with data science and engineering teams to standardize environments and streamline the transition from laboratory to production;

• Set forth standards, documentation, and MLOps/LLMOps best practices for various teams.

⛳️ Requirements

• Solid experience as an MLOps, Data, or Platform Engineer, specifically with production model deployments;

• Proficient in Python and knowledgeable about software engineering best practices such as testing, version control, and code review;

• Experience with pipeline orchestration tools (e.g., Airflow, Databricks Workflows) and CI/CD systems (GitHub Actions, Azure DevOps, or similar);

• Familiarity with model versioning and model registry systems (e.g., MLflow) and production monitoring;

• Background in large-scale data processing (such as Spark) and analytical data modeling.

• **Differentials (strong preference for Databricks):**

• Practical experience utilizing the Databricks platform (including Unity Catalog, Delta Lake, managed MLflow, Model Serving, Workflows, and clusters);

• Expertise in optimizing costs and performance of Spark jobs and clusters on Databricks;

• Experience with feature stores, drift monitoring, and frameworks for model evaluation.

🏝️ Benefits

• Competitive salary and performance-based incentives;

• Opportunities for professional growth and development;

• Flexible working hours and remote work options;

• Comprehensive health and wellness programs;

• Collaborative and inclusive company culture.