Lead MLOps DevOps Engineer | Bangalore | 8-12 Years

Job Title: Lead MLOps DevOps Engineer

Work Location: Bangalore, India

Experience Range: 8-12 Years

What We're Looking For

This lead-level role owns the design and operation of MLOps and DevOps platforms that support reliable model build, test, deployment, and release workflows. The position combines cloud-native infrastructure, container orchestration, automation, and production model serving to improve delivery speed, governance, and operational stability across machine learning environments.

Key Responsibilities

Design and operate scalable MLOps pipelines that support reliable build, test, deployment, and release processes for machine learning workloads.

Lead the implementation of cloud-native infrastructure, container orchestration, and automation practices across development and production environments to improve scalability and repeatability.

Establish monitoring, logging, and alerting standards for model and platform services to improve reliability, performance, and incident response.

Partner with data science, engineering, and security teams to productionize machine learning workloads with governance, repeatability, and compliance.

Define deployment standards for model serving, artifact promotion, and environment parity to reduce release risk and accelerate production adoption.

Drive technical best practices, mentor engineers on platform automation patterns, and continuously improve delivery speed and operational efficiency.

Strengthen secrets handling, access controls, and release governance across CI/CD and runtime environments to improve security and audit readiness.

Must-Have Skills

MLOps & Model Lifecycle: MLOps, Model deployment and serving

Cloud-Native Infrastructure & Orchestration: Containers, Kubernetes

Delivery Automation & Release Engineering: CI/CD pipelines, Artifact and package management

Infrastructure Provisioning & Security: Infrastructure as Code, Secrets management

Monitoring and observability

Python

Technical Skills

Operating Systems & Scripting: Linux, Bash and Python

Version Control & Source Management: Gitlab, GitHub

Cloud Platforms: AWS (Amazon Web Services)

Containerization & Deployment: Docker, Kubernetes

Infrastructure Automation & Configuration Management: Terraform, Ansible, CloudFormation

CI/CD Platforms: GitLab CI/CD

Observability Tools: DataDog or Dynatrace

MLOps Platforms & Workflow Orchestration: MLflow, Airflow, AWS SageMaker

Data & API Integration: SQL, REST APIs

Cloud Security & Access Control: IAM and cloud security controls

Why Join This Opportunity?

Own the platform layer that turns machine learning models into reliable production services.

Influence DevOps and MLOps standards across build, deployment, observability, and governance workflows.

Work at lead level on cloud-native automation and model-serving patterns that improve release speed and operational stability.