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.