MLOps Lead

The National e-Governance Division, Digital India Corporation; India; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

The National e-Governance Division at Digital India Corporation is seeking an accomplished ML Ops/DevOps engineer to design and manage CI/CD pipelines for AI/ML models. You will implement model versioning, deployment, monitoring, and rollback while enabling automated training and serving workflows across hybrid environments.

Collaborating with Data Science and Engineering teams, you will enforce standardized model inputs/outputs and performance metrics, plus governance for Responsible AI, bias

Qualifications

• 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.

Responsibilities

• Design and manage CI/CD pipelines for AI/ML models across environments.
• Establish model versioning, deployment, monitoring, and rollback mechanisms.
• 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 I/O formats and metrics.
• Implement logging, monitoring, and alerting for deployed models to ensure availability and accuracy.
• Ensure compliance with Responsible AI guidelines including bias auditing and explainability tracking.

Skills

Open source contributions

ML Ops focus

Education

B.Tech / M.Tech / M.S. in Computer Science or related discipline

Cloud DevOps or MLOps certification

Tools

MLflow

Kubeflow

Azure ML

AWS SageMaker Pipelines

GCP Vertex AI Pipelines

Docker

Kubernetes

Helm

Terraform

Seldon

TorchServe

Job description

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