National e-Governance Division
Create a free account
One signup unlocks everything for this job:
- See your personalized profile match score
- See the skills this role is missing for you
- Apply to this job and track your application
- Generate an AI cover letter for this role
Delhi · On-site · 4-7 Yrs · Sep 29
1 to 50 Employees · New Delhi, India
Hiring: Engineering
What employees say
+ This internship provides a great opportunity for learning, skilling, cross team communication, Government decision making process, Mainly how to balance work and personal time.
− Main issue is with irregular stipend receiving. Interns will receive every month stipend but do not expect that will be on time. There will be delays in receiving stipend but will
More reviews on the company profile.
Interview experience · Business Analyst
Online interview with 5-6 people taking the interview. Questions about system design , agile and apis. Also ask about negd products. Scenario based questions asking for solution design for specific use cases
Average difficulty · New Delhi · 2 reports
More interview insightsJob description
Overview
Educational Qualification • B.Tech / M.Tech / M.S.
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.
- 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
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.
Education
- 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.
Verify Python for this role
Free PRACTICE for Python adds a verified skill signal that can boost how roles like this rank for you — optional, not required to browse or apply.
Sign up — verify Python free →Want match scores and deeper insight? Create a free candidate profile
Job application FAQ
Last updated 24 October 2026
- How do I apply for MLOPs Lead at National e-Governance Division?
- Open this job on Feedback AI and sign up free (or sign in). You can apply from the job detail page; match scores and skill gaps appear when your profile is complete. National e-Governance Division may attach structured assessments to interview rounds after you apply.
- Does Feedback AI show a match score for this MLOPs Lead role?
- Yes after you add a resume or skills. Match score ranks this role against your profile — skills met, gaps, level, and location — so you prioritise applications where fit is strongest.
- Is MLOPs Lead remote or on-site in New Delhi, Delhi?
- This listing is marked on_site with location New Delhi, Delhi. Confirm details on the job description before applying.
- What skills matter for this MLOPs Lead role at National e-Governance Division?
- This is a lead MLOPs Lead role. Key skills include skills listed in the job description. Feedback AI highlights matched vs missing skills on your profile after signup.