Role Overview :

The MLOps is responsible for building and maintaining the full machine learning lifecycle across cloud and on-prem environments. This includes integrating data pipelines, labeling flows, training pipelines, testing frameworks, model optimization, deployment to cloud and embedded hardware, and monitoring of model performance. The role works closely with AI Engineers, Framework Engineers, Application Engineers, Data Engineering, and Label Management to enable scalable, reliable, and production-ready ML systems for autonomous driving.

Key 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.

Model Deployment :

- Establish deployment pipelines for ML models to :

- Cloud platforms (Azure/AWS)

- On-prem HPC/GPU clusters (Kubernetes, Slurm, NVIDIA infrastructure)

- 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 :

- AI Engineers on training and evaluation

- Framework Engineers on model optimization

- Application Engineers integrating ML outputs into the autonomy stack

- Data Engineering on scalable data and storage architecture

- ML Architect on 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 Masters degree in Engineering, Computer Science, Robotics, or related field.

- 5+ years of experience in data labeling, data operations, data quality, or ML dataset management preferably in autonomous driving.

- Strong 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.