Lead MLOps Engineer

4 weeks ago

India MathCo Full time

As a Lead MLOps Engineer, you will play a pivotal role in building scalable and reliable machine learning infrastructure for enterprise-grade applications. We are looking for a Lead Data Engineer with strong exposure to MLOps practices, ideally someone with a core data engineering background who has worked on large-scale data platforms. This is a hybrid role that blends big data engineering with end-to-end model lifecycle management- from development and deployment to monitoring and retraining. The ideal candidate will bring hands-on experience with Databricks, PySpark, and the orchestration of production-grade ML pipelines, enabling efficient and resilient solutions in dynamic, data-driven environments. Roles and Responsibilities - Design and implement distributed data processing pipelines using PySpark. - Collaborate with business architects and stakeholders to design scalable data and ML workflows. - Optimize performance of Spark applications through tuning, resource management, and caching strategies. - Debug long-running Spark jobs using Spark UI; address OOM errors, data skew, shuffle issues, and job retries. - Manage model deployment workflows using tools like MLflow for tracking, versioning, and registry. - Build and maintain CI/CD pipelines for both data and ML workflows. - Containerize applications using Docker and orchestrate using tools like Kubernetes. - Monitor production models, manage retraining workflows, and handle dependency management. - Contribute to clean, collaborative Git workflows with practices such as branching, rebasing, and PR reviews. - Work across teams to ensure models are production-ready, scalable, and aligned with business goals. - Develop and orchestrate big data workflows on Databricks. - Work on at least one cloud platform (preferably Azure) for scalable data and ML solutions. Required Skills and Experience: - Proficient in PySpark, with strong experience in Spark performance tuning and optimization. - Strong expertise in Databricks for development, orchestration, and job monitoring. - Working knowledge of MLflow or similar tools for model lifecycle management. - Proficient in Python and SQL. - Deep understanding of distributed data systems, job scheduling, and fault tolerance. - Experience in working with structured/unstructured data formats like Parquet, Delta, and JSON. - Familiarity with feature stores, model monitoring, drift detection, and automated retraining workflows. - Strong command over Git and version control in multi-developer environments. - Experience with CI/CD tools for data and ML pipelines. - Knowledge of containerization (Docker) and orchestration (Kubernetes) is a plus. - Experience with at least one major cloud platform (Azure preferred, or AWS/GCP).

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