MLOps Engineer

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.

The core responsibilities for the job include the following:

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 and 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, and 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 cloud platforms (Azure/AWS), on-prem HPC/GPU clusters (Kubernetes, Slurm, and NVIDIA infrastructure), and embedded compute platforms (NVIDIA Orin).

Monitoring and 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 and Cross-Functional Alignment:

• Work closely with AI engineers in training and evaluation, framework engineers in model optimization, application engineers integrating ML outputs into the autonomy stack, data engineers on scalable data and storage architecture, and ML architects 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.

Requirements:

• Bachelor's or master's degree in engineering, computer science, robotics, or a related field.
• 5+ years (tech lead) 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.