MLOps Engineer

ROLE OVERVIEW
The MLOps Engineer will operationalise AI/ML models by building reliable deployment pipelines, managing settings, 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 workplace 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