MLOps Lead / Engineer

National e-Governance Division (NeGD), Digital India Corporation; Delhi; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

National e-Governance Division (NeGD), Digital India Corporation is seeking an experienced ML Ops/DevOps engineer to lead end-to-end ML lifecycle automation in government ecosystems.

You will design and manage CI/CD pipelines for AI/ML models, implement model versioning, deployment, monitoring, and rollback strategies, and ensure scalable on-prem or cloud deployment with governance-compliant practices.

Qualifications

• 7–10 years in machine learning operations or DevOps engineering.
• Minimum 4 years building CI/CD pipelines for AI/ML deployments.
• 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 for 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.
• Ensure compliance with Responsible AI guidelines for deployment including bias auditing and explainability tracking.

Skills

CI/CD pipelines

MLOps

Python automation

Containerization

Kubernetes

Education

B.Tech / M.Tech / MS in Computer Science, Data Engineering, AI or related

Cloud DevOps or MLOps certifications (AWS/Azure/GCP)

Tools

MLflow

Kubeflow

Azure ML

AWS SageMaker Pipelines

GCP Vertex AI Pipelines

Docker

Kubernetes

Terraform

CloudFormation

Ansible

TorchServe

TensorFlow Serving

Seldon

KServe

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