Lead MLOps Engineer for Autonomous Driving Pipelines

Epam Systems; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Epam Systems is seeking an experienced MLOps lead to build and maintain end-to-end ML pipelines spanning data ingestion, labeling, training, deployment, and monitoring for autonomous driving systems.

The role emphasizes delivering reproducible pipelines across cloud and on-prem environments, collaborating with AI, data engineering, and framework teams, and ensuring robust ML operations and dashboards for performance visibility.

Qualifications

• Bachelor's or Master's degree in engineering, CS, robotics, or related field.
• 5+ years of experience in data labeling, data operations, 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.

Responsibilities

• 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.
• 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.
• 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).
• 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.
• Collaborate with AI Engineers, Framework Engineers, Application Engineers, Data Engineering, ML Architect to align and implement components.
• Drive best practices for MLOps, documentation, and standardized ML workflows across the department.
• Steer and guide MLOPS engineers.

Skills

Python

C++

MLOps

Docker

Kubernetes

CI/CD

Distributed training

Data labeling

Data pipelines

Education

Engineering/CS/Robotics degree

Tools

MLflow

Kubeflow

Airflow

Argo

Azure ML

Databricks

Job description

Epam Systems is seeking an experienced MLOps lead to build and maintain end-to-end ML pipelines spanning data ingestion, labeling, training, deployment, and monitoring for autonomous driving systems.

The role emphasizes delivering reproducible pipelines across cloud and on-prem environments, collaborating with AI, data engineering, and framework teams, and ensuring robust ML operations and dashboards for performance visibility.