About the Role

Experience: 3-10 years

Location: Whitefield, Bengaluru (in-person interviews)

Immediate joiner or notice up to 15 days preferred

Own the path from trained model to production. We are hiring an MLOps Engineer for our client to build and run the infrastructure that takes ML/AI models from experimentation to reliable, monitored production systems on Azure.

This is a hands-on role: you will build real pipelines, deploy real models, and keep them healthy in production. If you have shipped ML systems that people actually use, and you enjoy owning the full lifecycle from data ingestion to model monitoring, we would like to talk.

What you will do

- Build and own end-to-end ML pipelines: data ingestion, training, evaluation, packaging, versioning, deployment
- Design and maintain multi-stage CI/CD for ML using Jenkins and Azure DevOps, with GitOps and pre-commit workflows
- Deploy and serve models with FastAPI, Docker, and Kubernetes (AKS)
- Run the Databricks ML platform: pipelines, Workflows, Delta Lake, MLflow, Asset Bundles, PySpark
- Monitor production models for drift and performance (Evidently AI, Prometheus, Grafana, Azure Monitor)
- Apply DevSecOps practices:



SonarQube, Trivy/Snyk, Azure Key Vault, Managed Identity
- Work closely with data scientists and engineers to productionize GenAI/LLM workloads

What you must bring:

- 3-10 years in MLOps / ML Engineering with hands-on production experience
- Robust Python and scripting (Bash, YAML, SQL)
- CI/CD for ML systems: Jenkins and/or Azure DevOps, Git/GitOps
- Azure (or AWS/GCP with willingness to work in Azure)
- Databricks and MLflow
- Docker and Kubernetes (AKS preferred)
- Model deployment and serving (FastAPI, REST APIs)
- Genuine experience building, deploying, operating and maintaining production ML/AI systems - not only DevOps or cloud work
- Based in Bengaluru or willing to relocate - interviews are in person at Whitefield, Bengaluru
- Immediate joiner or notice up to 15 days

Good to have:

- vLLM, ONNX, TensorRT, inference optimisation
- Airflow, DVC, W&B;/Comet ML, Terraform, KEDA
- Azure APIM, Azure AD/RBAC, Ray Serve, BentoML
- LLM fine-tuning pipelines
- Groovy/Jenkinsfile
- Manufacturing or semiconductor domain exposure

📌 MLOps Engineer (3+ yrs) (Bengaluru)
🏢 RoundCircle
📍 Bengaluru