ML Ops Lead

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. 2. Build and maintain automated pipelines for continuous integration and delivery (CI/CD). 3. Implement real-time monitoring for model drift, latency, and inference performance. 4. Collaborate with Solution Architect and MLOps Lead to standardize deployment infrastructure. 5. Ensure reproducibility, rollback, and version control for deployed models. 6. Integrate AI services with NeGDs standard APIs and observability frameworks. 7. Maintain deployment logs, error reports, and environment 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.