MLOPs Lead

Educational Qualification

• 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