- B.Tech / M.Tech / M.S. in Computer Science, Data Engineering, AI or related discipline.

- Certification in cloud DevOps or MLOps platforms (AWS DevOps Engineer, Azure DevOps Expert, GCP Professional ML Engineer) is highly desirable.

- Contributions to MLOps or DevOps open source projects is preferred.

Experience :

- 7 - 10 years in machine learning operations or DevOps engineering.

- Minimum 4 years building CI/CD pipelines for AI/ML model deployment in enterprise or government ecosystems.

- Proven experience with containerized and microservice architectures.

Key Responsibilities :

- Design and manage continuous integration and delivery (CI/CD) pipelines for AI/ML models across multiple environments.

- Establish model versioning, deployment, monitoring, and rollback mechanisms to ensure stability and traceability.

- Automate training, testing, and serving workflows using containerized solutions.

- Define infrastructure-as-code templates for scalable AI deployment on on-prem or cloud environments.

- Collaborate with Data Science and Engineering teams to standardize model input/output formats and performance metrics.

- Implement logging, monitoring, and alerting for deployed models to ensure high availability and accuracy over time.

- Ensure compliance with Responsible AI guidelines for deployment, including bias auditing and explainability tracking.

Technical Competencies :

- MLOps Platforms : MLflow, Kubeflow, Azure ML, AWS SageMaker Pipelines, GCP Vertex AI Pipelines for end-to-end ML workflow orchestration

- Containerization : Docker, Kubernetes, Helm charts, container registries, and microservices architecture for ML workloads

- CI/CD : Jenkins, GitLab CI, GitHub Actions, Azure DevOps with specialized ML pipeline integration and automated testing

- Infrastructure-as-Code : Terraform, CloudFormation, Ansible for reproducible ML infrastructure provisioning and management

- Cloud Platforms : AWS (EKS, Lambda, ECR, S3), Azure (AKS, Container Registry, Blob Storage), GCP (GKE, Cloud Build, Cloud Storage)

- Model Serving : TorchServe, TensorFlow Serving, Seldon, KServe, REST APIs, and real-time inference infrastructure.

- Programming Languages : Python for automation, Bash scripting, YAML for configuration management, basic understanding of Go/Java

- Database & Storage : Feature stores (Feast, Tecton), model registries, data versioning (DVC), and distributed storage systems

- Workflow Orchestration : Apache Airflow, Prefect, Argo Workflows for complex ML pipeline scheduling and dependency management