ML Ops Engineer - A decision intelligence firm helping organizations turn data into business outcomes using analytics, AI, and engineering-led solutions across marketing, supply chain, and customer intelligence.

As an 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 Senior 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. Key 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. Internal Use Only • 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).