Job Description
About the Role
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nWe are looking for a hands-on MLOps Engineer / Developer to build and operate production-grade ML and GenAI infrastructure. You will work closely with AI/ML teams to take models from experimentation to production through robust pipelines, CI/CD, model serving, monitoring, and cloud deployment
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nThe ideal candidate should have strong experience with Python, Jenkins, Azure, Databricks, FastAPI, Docker, Kubernetes/AKS and MLflow, along with a valuable understanding of DevSecOps and production ML systems.
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nKey Responsibilities
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n• Build end-to-end pipelines — data ingestion, training, evaluation, packaging, versioning, and deployment.
n• Develop and maintain Jenkins CI/CD for Dev → QA → Production promotion with multi-stage gates.
n• Deploy model-serving APIs on AKS using FastAPI and vLLM; apply ONNX/TensorRT optimizations.
n• Set up observability — drift detection (Evidently AI), Prometheus/Grafana, Azure Monitor.
n• Apply DevSecOps practices — Key Vault, Managed Identity, SonarQube, Trivy/Snyk.
n• Application Development – REST,



WebSocket Frameworks using FastAPI
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nMust-Have Skills
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n• 4+ years in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.
n• CI/CD tooling: CI tooling (UV, Ruff, Pyrefly), Jenkins (strong), Azure DevOps, GitOps concepts; Git and pre-commit workflows.
n• Databricks ML pipelines (Delta Lake, Workflows, MLflow), Asset Bundles and PySpark for data processing.
n• Model serving: FastAPI, Docker, AKS; exposure to vLLM and ONNX/TensorRT optimization.
n• Python (strong — FastAPI, Pydantic, async), Bash, YAML/SQL scripting.
n• Cloud knowledge – Azure/AWS/GCP Storage, AI related services.
n• Databases and storage: PostgreSQL, Redis, ADLS Gen2.
n• Understanding of containerization, Helm, and infrastructure automation.
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nGood to Have
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n• Airflow, DVC, and experiment tracking (W&B; / Comet ML).
n• Terraform, KEDA, Azure APIM, and AAD RBAC configuration.

📌 MLOps Engineer (Bengaluru)
🏢 RoundCircle
📍 Bengaluru