MLOps Engineer

Expertshub Ai; Delhi; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Expertshub Ai in Delhi is seeking an experienced MLOps Engineer to operationalise AI/ML models by building reliable deployment pipelines, managing environments, and ensuring reproducibility across development, staging, and production. Proficiency with cloud ML platforms and container orchestration is required.

You will implement drift and performance monitoring, collaborate with architects and platform teams, and maintain logs, audit trails, and governance to support scalable, responsible AI

Qualifications

• Bachelor's or master's degree in a relevant field.
• Cloud/DevOps certifications preferred.
• Evidence of AI/ML projects or publications advantageous.

Responsibilities

• Deploy and manage AI/ML models across development, staging, and production environments.
• Build 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, environment 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.

Skills

CI/CD pipelines

MLOps

Model monitoring

Governance & audit

Education

B.Tech/M.Tech in CS/AI

Cloud certifications (AWS/Azure/GCP)

Research/open-source contributions

Tools

Docker

Kubernetes

MLflow

Kubeflow

Terraform

Git

Job description

MLOps Engineer

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.

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, environment 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.