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Job details:
Key Responsibilities
- Deploy and manage AI/ML models in development, staging, and production
environments.
- Build and maintain automated pipelines for continuous integration and delivery (CI/CD). 3. 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 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.
Skills: mlops,ci/cd,infrastructure,ai/ml models,api,models,ml
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