MLOps Engineer

ROLE OVERVIEW
The MLOps Engineer will operationalise AI/ML models by building reliable deployment pipelines, managing environments, monitoring production performance, and ensuring reproducibility, version control, rollback, observability, and audit readiness.

Educational Qualifications

- B.Tech. or M.Tech. in Computer Science, AI/ML, or a related discipline.
- DevOps or cloud-infrastructure certifications in AWS, Azure, or GCP are preferred.
- Research papers, case studies, or meaningful open-source contributions are advantageous.

Experience

- 36 years of experience operationalising AI/ML models and automating CI/CD pipelines.
- Experience deploying ML pipelines for NLP, computer vision, speech, or similar workloads.
- Familiarity with model monitoring, lifecycle management, performance logging, and production support.

Key Responsibilities

- Deploy and manage AI/ML models across development, staging, and production environments.
- Build, automate, and maintain CI/CD pipelines for model training, testing, packaging, deployment, and release.
- Implement monitoring for drift, latency, throughput, inference quality, resource utilisation, and service availability.
- Collaborate with Solution Architects, MLOps Leads, ML engineers,



and platform teams to standardise deployment patterns.
- Ensure reproducibility, model and data versioning, setting consistency, rollback, and release traceability.
- Integrate AI services with NeGD-standard APIs, logging, monitoring, and observability frameworks.
- Maintain deployment logs, error reports, run histories, configuration records, and environment snapshots for audit readiness.
- Support container orchestration, infrastructure automation, model registries, and production incident resolution.
- Apply Responsible AI traceability and governance controls within deployment workflows.

Technical Competencies

- Infrastructure and Containers: Jenkins, GitLab CI/CD, GitHub Actions, Docker, Kubernetes, and Terraform.
- Monitoring and Logging: Prometheus, Grafana, ELK Stack, and alerting practices.
- ML Lifecycle: MLflow, Kubeflow, DVC, model registries, and experiment tracking.
- Cloud Platforms: AWS SageMaker, Azure Machine Learning, and GCP Vertex AI.
- Core Engineering: Python, Bash, YAML/JSON configuration, Linux administration, and API integration.
- Governance: deployment traceability, approval gates, audit logging, and Responsible AI compliance.

📌 MLOps Engineer (New Delhi)
🏢 Expertshub.ai
📍 New Delhi