Lead MLOps DevOps Engineer | Bangalore | 8-12 Years

Job description

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.