Senior MLOps Engineer

About the Role

We are looking for an experienced Senior MLOps Engineer to design, build, and manage scalable machine learning infrastructure and production ML systems. The candidate will be responsible for automating the end-to-end machine learning lifecycle, including model development, training, deployment, monitoring, and continuous improvement. The role involves collaborating with Data Scientists, Data Engineers, ML Engineers, and DevOps teams to implement reliable ML pipelines, establish MLOps best practices, optimize model performance, and deploy cloud-native machine learning solutions at scale.

Responsibilities

• Design and implement end-to-end MLOps pipelines for model training, validation, deployment, and monitoring.
• Build automated CI/CD/CT workflows for machine learning models.
• Deploy, scale, and manage ML models in production environments.
• Implement model versioning, experiment tracking, and model registry solutions.
• Monitor model performance, data quality, model drift, and production reliability.
• Develop scalable ML infrastructure using Docker, Kubernetes, and cloud platforms.
• Optimize model serving, inference latency, resource utilization, and deployment costs.
• Implement data and feature pipelines using appropriate orchestration and processing tools.
• Establish ML governance, reproducibility, security, and compliance practices.
• Collaborate with cross-functional teams to improve ML platform reliability and operational efficiency.
• Provide technical leadership, define MLOps architecture, and mentor engineering teams.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• 10+ years of experience in software engineering, ML engineering, MLOps, or related disciplines.
• Proven experience building and operating production-grade machine learning systems.
• Strong understanding of machine learning lifecycle management, distributed systems, and cloud infrastructure.
• Experience designing scalable, reliable, and automated ML platforms.

Nice to Have

• Generative AI, LLMOps, and large language model deployment.
• Retrieval-Augmented Generation (RAG) pipelines and vector databases.
• Distributed model training and GPU optimization.
• Model serving tools such as KServe or NVIDIA Triton Inference Server.
• Experience with feature stores and real-time inference systems.
• ML security, governance, explainability, and responsible AI.
• Experience leading engineering teams and defining enterprise MLOps strategies.

Pay: ₹2,574,000.00 - ₹3,050,000.00 per year

Benefits:

• Health insurance
• Life insurance
• Provident Fund

Ability to commute/relocate:

• Bangalore City, Bengaluru, Karnataka: Reliably commute or planning to relocate before starting work (Required)

Experience:

• MLOps: 10 years (Required)

Work Location: Hybrid remote in Bangalore City, Bengaluru, Karnataka