Senior System Emgineer

Epam Systems; Bengaluru; Hybrid; INR 1,800,000 - 3,400,000; Full time

Job summary

Epam Systems in Bengaluru is seeking a Senior MLOps lead to design and build end-to-end ML pipelines, covering data ingestion, labeling workflows, training, validation, deployment, and monitoring across cloud and on-prem GPU clusters.

You will integrate data pipelines with labeling platforms, enforce quality gates with the Label Manager, and collaborate with AI, Framework, and Data engineers to drive reusable MLOps practices.

Qualifications

• Bachelor's or Master’s degree in Engineering, Computer Science, Robotics, or related field.
• 5+ years 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.

Responsibilities

• End-to-End MLOps pipeline development, from data ingestion to deployment and monitoring.
• Build and maintain automated end-to-end ML pipelines across cloud and on-prem GPUs.
• Integrate data pipelines with labeling platforms and automate dataset creation and quality checks.
• Collaborate with Label Manager to enforce labeling quality gates and track dataset KPIs.
• Automate training workflows, hyperparameter tuning, experiment tracking, and benchmarking.
• Implement optimization steps (quantization, compilation, pruning, distillation) with Framework Engineer.
• Establish deployment pipelines for cloud, edge, and embedded devices (Azure, Kubernetes, Slurm, NVIDIA).
• Develop automated monitoring for model/data drift and performance, with dashboards and alerts.
• Lead MLOps engineers, promote best practices, and drive standardized ML workflows across the department.

Skills

Python

C++

MLOps

Docker

Kubernetes

CI/CD

Azure ML

Data labeling

Distributed training

Databricks

Education

Bachelor's or Master's in Engineering/CS/Robotics or related field

Tools

MLflow

Kubeflow

Airflow

Argo

Azure ML

Databricks

Docker

Kubernetes

Job description

Role & responsibilities

• End-to-EndMLOpsPipeline 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 forML modelsto:
  • Cloud platforms (Azure)
  • On-prem HPC/GPU clusters (Kubernetes,Slurm, NVIDIA infrastructure)
  • Embeddedcomputeplatforms (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 forMLOps, documentation, and standardized ML workflows across the department.
• Steerand guide MLOPS engineers.

Required Skills & Experience

• Bachelors orMaster’s degree in Engineering, Computer Science, Robotics, or relatedfield.
• 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 MLOpsframeworks: MLflow, Kubeflow, Airflow, Argo, Azure ML, Databricksorsimilar.
• 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