Lead Data Science Engineer specializing in Machine Learning Operations (MLOps)

We are seeking a Lead Data Science Engineer specializing in Machine Learning Operations (MLOps) to join our growing Data & AI practice. In this role, you will own the end-to-end ML lifecycle - from experimentation and model development to automated deployment and production monitoring - using MLflow as the central platform for experiment tracking, model registry, and deployment orchestration.

Leadership & Collaboration

• Lead a team of 3-6 ML engineers and data scientists; conduct design reviews and mentor junior talent

• Collaborate with client stakeholders to gather requirements, translate them into ML system architecture, and communicate trade-offs

• Define MLOps maturity roadmaps for client engagements and internal projects

Overall 12+ yrs of experience

• 8+ years of experience in data science, ML engineering, or a closely related discipline

• 5+ years of hands-on MLflow usage across Tracking, Projects, Models, and Registry components

• Strong proficiency in Python; experience with ML frameworks: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow

• Demonstrated experience building production-grade ML pipelines on at least one major cloud platform (AWS, Azure, Google Cloud Platform)

• Deep knowledge of containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform, Helm)

• Experience with feature store design, data versioning (DVC), and model governance frameworks

• Strong SQL and working knowledge of distributed computing (Spark, Dask)

• Excellent communication skills; ability to present technical concepts to executive and non-technical audiences