Machine Learning Engineer

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.

• 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).