MLOPs Lead

National e-Governance Division; New Delhi; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

National e-Governance Division seeks a seasoned ML Ops Engineer to design and run CI/CD pipelines for AI/ML models across on‑prem and cloud environments. You will implement model versioning, monitoring, and rollback to ensure reliability, while automating training, testing, and serving workflows using containerized tools.

Responsibilities include infrastructure‑as‑code for scalable deployment, collaboration with data science teams on inputs/metrics, and adherence to Responsible AI practices for

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 continuous integration and delivery (CI/CD) pipelines for AI/ML models across multiple environments.
• Establish model versioning, deployment, monitoring, and rollback mechanics 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.

Skills

ML Ops

CI/CD for AI/ML

Docker/Kubernetes

MLOps platforms

Infrastructure as Code

Model serving

Programming: Python

Data/versioning

Workflow orchestration

Go/Java basics

YAML

Education

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

Tools

MLflow

Kubeflow

Azure ML

SageMaker Pipelines

Vertex AI Pipelines

Docker

Kubernetes

Helm

Terraform

CloudFormation

Ansible

TorchServe

TF Serving

Seldon

KServe

REST APIs

Job description

• 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 mechanics 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.
• 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.
• 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