This is for Govt of India National E-Governance Dept.

If you are from other than Bangalore, it is also fine. Location can be any major city, depending upon specific part of AI mission or hybrid.

Designation: AI/ML Solution Architect

How to apply?

Go to this link. We have our internal tool to take through the flow. There is an optional but preferred AI screening (there is no AI auto rejection)

https://app.careerplan.app/#/a/gDfwQGwXZ9kr?jt=MLOps%20Lead&cpReturnUrl;=%2Fjob&job;_reference_id=TC-JOB-20260901-JN5SCF

MLOps Lead

Designation: 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 Skilled 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

1. Design and manage continuous integration and delivery (CI/CD) pipelines for

AI/ML models across multiple environments.
1. Establish model versioning, deployment, monitoring, and rollback mechanisms to

ensure stability and traceability.
1. Automate training, testing, and serving workflows using containerized solutions.
2. Define infrastructure-as-code templates for scalable AI deployment on on-prem or

cloud environments.
1. Collaborate with Data Science and Engineering teams to standardize model





input/output formats and performance metrics.
1. Implement logging, monitoring, and alerting for deployed models to ensure high

availability and accuracy over time.
1. 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

Pay: ₹1,200,000.00 - ₹2,000,000.00 per year

Application Question(s)

- What is your notice period / lead time to join ?
- Do you have any offer in hand?

Work Location: Hybrid remote in Bengaluru, Karnataka

📌 MLOps Lead (Bengaluru)
🏢 Manomaya AI Systems
📍 Bengaluru