Role & responsibilities

- End-to-End MLOps Pipeline Development
- • Build and maintain automated end-to-end ML pipelines covering data ingestion, dataset management, labeling workflows, training, validation, optimization, deployment, and monitoring.
- • Ensure ML pipelines run in both cloud and on-prem GPU clusters, with strong reproducibility and traceability.
- Data & Labeling Integration
- • Integrate data pipelines with labeling platforms and automate dataset creation and quality checks.
- • Work closely with the Label Manager to enforce labeling quality gates and track dataset KPIs.
- Training, Testing & Optimization
- • Support AI Engineers by automating training workflows, hyperparameter tuning, experiment tracking, and benchmarking.
- • Integrate optimization steps (quantization, compilation, pruning, distillation) in collaboration with the Framework Engineer.
- Model Deployment
- • Establish deployment pipelines for ML models to:
- o Cloud platforms (Azure)
- o On-prem HPC/GPU clusters (Kubernetes, Slurm, NVIDIA infrastructure)
- o Embedded compute platforms (NVIDIA Orin)
- Monitoring & Observability
- • Develop automated monitoring systems for model drift, data drift, performance degradation, anomalies, and operational metrics.
- • Build dashboards and alerts to monitor model performance across simulation and on-vehicle tests.
- Collaboration & Cross-Functional Alignment
- • Work closely with:
- o AI Engineers on training and evaluation




- o Framework Engineers on model optimization
- o Application Engineers integrating ML outputs into the autonomy stack
- o Data Engineering on scalable data and storage architecture
- o ML Architecton integration and implementation of new components
- • Drive best practices for MLOps, documentation, and standardized ML workflows across the department.
- • Steer and guide MLOPS engineers.
- Required Skills & Experience
- • Bachelors or Master’s degree in Engineering, Computer Science, Robotics, or related field.
- • 5+ years(Tech lead) of experience in data labeling, data operations, data quality, or ML dataset management—preferably in autonomous driving.
- • Robust programming skills in Python, with familiarity in C++ preferred.
- • Hands-on experience with MLOps frameworks: MLflow, Kubeflow, Airflow, Argo, Azure ML, Databricks or similar.
- • Strong understanding of Docker, Kubernetes, CI/CD, and distributed training.
- • Experience with data pipeline technologies (Kafka, Spark, Delta Lake, Databricks).
- • Good understanding of multimodal autonomous driving data (camera, LiDAR, radar).
- • Proven experience deploying models to cloud, edge, or embedded devices.
- • Experience with real-time systems and embedded CI/CD pipelines.

MUST HAVE SKILLS: Data Devops with Azure, Docker, Kubernetes, python, MLOPS, Continuous Integration

Preferred candidate profile

Perks and advantages

📌 Senior System Emgineer (Bengaluru)
🏢 EPAM Systems
📍 Bengaluru