Mlops Engineer Bengaluru

Mlops Engineer Bengaluru

11 Sep
|
RoundCircle
|
Bengaluru

11 Sep

RoundCircle

Bengaluru

About the Role
We are looking for a hands-on MLOps Engineer / Developer to build and operate production-grade ML and Gen AI 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
The ideal candidate should have strong experience with Python, Jenkins, Azure, Databricks, Quick API, Docker, Kubernetes/AKS and MLflow, along with a good understanding of Dev Sec Ops and production ML systems.
Key Responsibilities
Build end-to-end pipelines — data ingestion, training, evaluation, packaging, versioning, and deployment. Develop and maintain Jenkins CI/CD for Dev → QA → Production promotion with multi-stage gates. Deploy model-serving APIs on AKS using Quick API and v LLM; apply ONNX/Tensor RT optimizations. Set up observability — drift detection (Evidently AI), Prometheus/Grafana, Azure Monitor. Apply Dev Sec Ops practices — Key Vault, Managed Identity, Sonar Qube, Trivy/Snyk. Application Development – REST,



Web Socket Frameworks using Fast API Must-Have Skills
4+ years in ML/AI engineering or Dev Ops with hands-on production MLOps pipeline experience. CI/CD tooling: CI tooling (UV, Ruff, Pyrefly), Jenkins (robust), Azure Dev Ops, Git Ops concepts; Git and pre-commit workflows. Databricks ML pipelines (Delta Lake, Workflows, MLflow), Asset Bundles and Py Spark for data processing. Model serving: Quick API, Docker, AKS; exposure to v LLM and ONNX/Tensor RT optimization. Python (strong — Fast API, Pydantic, async), Bash, YAML/SQL scripting. Cloud knowledge – Azure/AWS/GCP Storage, AI related services. Databases and storage: Postgre SQL, Redis, ADLS Gen2. Understanding of containerization, Helm, and infrastructure automation. Good to Have
Airflow, DVC, and experiment tracking (W& B / Comet ML). Terraform, KEDA, Azure APIM, and AAD RBAC configuration. LLM fine-tuning pipelines; Ray Serve or Bento ML exposure. Groovy (Jenkinsfile). Manufacturing or semic

📌 Mlops Engineer Bengaluru
🏢 RoundCircle
📍 Bengaluru

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