Educational Qualification: . B.Tech / M.Tech in Computer Science, AI/ML, or related discipline.

- Certifications in DevOps or Cloud Infrastructure (AWS, Azure, or GCP) preferred.

- Research papers, case studies, or significant open source contributions(are preferred) Experience: . 7-10 years in operationalizing AI/ML models with proven experience in CI/CD automation. . Prior exposure to deploying ML pipelines for NLP, computer vision, or speech systems. . Familiarity with monitoring, model lifecycle management, and performance logging.

Key Responsibilities: 1. Deploy and manage AI/ML models in development, staging, and production environments.

- Build and maintain automated pipelines for continuous integration and delivery (CI/CD).
- Implement real-time monitoring for model drift, latency, and inference performance.
- Collaborate with Solution Architect and MLOps Lead to standardize deployment infrastructure.
- Ensure reproducibility, rollback, and version control for deployed models.




- Integrate AI services with NeGD's standard APIs and observability frameworks.
- Maintain deployment logs, error reports, and setting snapshots for audit readiness.

Technical

Competencies:

- Infrastructure Tools: Jenkins, GitLab CI/CD, Docker, Kubernetes. . Monitoring & Logging: Prometheus, Grafana, ELK Stack. .

ML Lifecycle Management: MLflow, Kubeflow, DVC. .

Cloud Platforms: AWS SageMaker, Azure ML Studio, GCP Vertex AI. .

Core: Python, Bash scripting, YAML/JSON configuration, Linux systems. .

CI/CD: Jenkins, GitLab CI, or GitHub Actions for automated deployments, Terraform .

Governance: Traceability and Responsible AI compliance in deployment.

Required Skills

['Core ML', 'Jenkins', 'Python', 'Bash']

Additional Information

Use public JD while sourcing.

📌 ML Ops Lead (Bawana)
🏢 HuntingCube
📍 Bawana