Role Overview
This role requires 7 10 years of experience, with strong hands-on expertise in production machine learning deployment, MLOps platforms, and cloud infrastructure.
The candidate will be responsible for designing, implementing, and managing end-to-end ML deployment pipelines, ensuring scalability, automation, reliability, and monitoring of production machine learning systems.
Educational Qualification
(link removed) in Computer Science, AI/ML, Information Technology, or a related discipline.
Cloud, DevOps, or MLOps certifications are preferred.

Mandatory Requirements
Experience building and managing production ML deployment platforms and CI/CD/CT pipelines.
Strong knowledge of containerization, orchestration, cloud infrastructure, monitoring, and automation.
Experience with model versioning, reproducibility, model lifecycle management, and drift monitoring.
Ability to lead MLOps practices and collaborate with data science and engineering teams.

Key Responsibilities
Design and manage scalable infrastructure and workflows for deploying and operating ML models.
Build automated pipelines for testing, packaging, deployment, monitoring, and rollback.
Standardize model registry,



version control, reproducibility, release management, and workplace configuration.
Implement observability for model quality, latency, availability, cost, and drift.
Guide the team on reliability, security, governance, incident management, and continuous improvement.
Collaborate with data scientists, ML engineers, and platform teams to improve production deployment processes.
Optimize ML infrastructure for scalability, availability, and cost efficiency.

Technical Competencies

MLOps and Model Lifecycle
MLflow.
DVC.
Model Registry.
Model Versioning.
Model Lifecycle Management.
Model Monitoring.
Model Drift Detection.
Reproducibility.

Containerization and Orchestration
Docker.
Kubernetes.

CI/CD and Automation
Jenkins.
GitLab CI.
GitHub Actions.
Continuous Integration / Continuous Deployment.
Continuous Training.

Cloud and Infrastructure
AWS.
Azure.
GCP.
Terraform.
Infrastructure as Code.

Programming and Monitoring
Python.
Bash.
Linux.
Prometheus.
Grafana.
Monitoring and Observability.

📌 MLOPS LEAD (7 Years) (New Delhi)
🏢 Enormous Enterprise
📍 New Delhi