Lead MLOps Engineer

14 hours ago

Bengaluru, Karnataka, India MathCo Full time US$ 1,50,000 - US$ 2,00,000 per year

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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