Lead Machine Learning Engineer
Summary
MLOps Lead (7-10 yrs) who designs and runs production ML deployment platforms: building CI/CD/CT pipelines, model registries, automated deploy/rollback workflows, and observability for model quality and drift. Core stack: MLflow, DVC, Docker, Kubernetes, Terraform, Python, AWS/Azure/GCP, Prometheus/Grafana.
MLOps Lead
Location: New DelhiNCR
Experience: 710 years
Educational Qualification:
B.Tech/B.E./M.Tech 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 registries, version control, reproducibility, release management, and environment configuration.
- Implement observability for model quality, latency, availability, cost, and drift.
- Guide the team on reliability, security, governance, incident management, and continuous improvement.
Technical Competencies
MLflow, DVC, Docker, Kubernetes, Jenkins/GitLab CI/GitHub Actions, Terraform, Python, Bash, Linux, AWS/Azure/GCP, Prometheus, Grafana, and model monitoring.