Senior Machine Learning DevOps Engineer

We’re Hiring : Senior MLOps / DevOps Engineer (for one of our esteemed clients)

Location : PAN India (Gurugram, Noida, Bangalore, Chennai, Hyderabad, Pune)

Experience : 7+ Years

Domain : AI / ML Infrastructure & Automation

About the Company

We’re looking for a passionate and experienced Senior MLOps / DevOps Engineer to join our client’s growing team.

About the Role

You’ll be responsible for architecting, automating, and optimizing large-scale Data & ML pipelines on AWS, while ensuring performance, scalability, and reliability across the infrastructure.

Responsibilities

Build and maintain systems using DevOps & LLM Ops tools (Kubernetes, Docker, Terraform, AWS, Python).

Architect and implement AWS-based scalable infrastructure (Lambda, EC2, Docker, Networking, Monitoring, etc.).

Design and automate Data & ML pipelines with CI / CD and deployment best practices.

Implement observability solutions using Prometheus, Loki, Grafana, or Datadog.

Conduct POCs for new solutions and continuously improve existing architectures.

Collaborate with Product, Data, and Executive teams to address infrastructure and data challenges.

Drive code reviews and mentor junior engineers.

Qualifications

Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Machine Learning, or related field.

5–7+ years of experience in AI / ML development or production-level MLOps systems

Prior experience in AI-first companies or open-source ML contributions is a plus

Required Skills

7+ years of experience in Software / DevOps Engineering with strong Python, SQL, and Pandas skill.

Hands-on experience with AWS (Lambda, EC2, EMR, S3, Glue, Athena, RDS, IAM, Batch, Sagemaker, Airflow)

Expertise in Terraform, Kubernetes, Docker, GitHub Actions, Jenkins, or CodePipeline

Experience in CI / CD pipelines and working with API.

Familiarity with Relational & NoSQL databases (Snowflake preferred)

Exposure to Kubeflow or MLflow is an added advantage.

Note : Looking for Immediate Joiners only