Lead Machine Learning Engineer

Nisource Technologies; Dadri, New Delhi, Gurugram District; On-site; INR 1,500,000 - 1,900,000; Full time

Job summary

Nisource Technologies in New Delhi NCR is seeking an experienced MLOps Lead to design and manage scalable ML deployment infrastructure. You will build automated pipelines for testing, packaging, deployment, monitoring, and rollback, while ensuring reproducibility and reliability.

Lead and mentor data science and engineering teams, implement observability for model quality and drift, and govern model lifecycle and deployments across cloud environments.

Qualifications

• B.Tech/B.E./M.Tech in Computer Science, AI/ML or related discipline.
• Cloud, DevOps or MLOps certifications preferred.
• Strong knowledge of production ML deployment pipelines.
• Experience with model versioning and drift monitoring.

Responsibilities

• Design and manage scalable infrastructure for ML deployment.
• Build automated pipelines for testing, packaging, deployment, monitoring, and rollback.
• Standardize model registries and release management.
• Implement observability for model quality, latency, cost, and drift.
• Lead MLOps practices collaborating with data science and engineering teams.

Skills

ML deployment

CI/CD pipelines

MLOps leadership

Model lifecycle management

Drift monitoring

Automation

Education

B.Tech/B.E./M.Tech in CS/AI/ML

Tools

Docker

Kubernetes

Jenkins

GitHub Actions

GitLab CI

Terraform

Python

Cloud (AWS/GCP/Azure)

Prometheus

Grafana

MLflow

DVC

Job description

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.