MLOps+ AI Engineer (Bengaluru)

MLOps+ AI Engineer (Bengaluru)

26 Oct
|
RoundCircle
|
Bengaluru

26 Oct

RoundCircle

Bengaluru

About the Role

Build and operate the MLOps pipelines that take AI/ML and GenAI models from experimentation to production: packaging, CI/CD delivery, model serving and monitoring. A hands-on engineering role bridging data science and enterprise deployment.

Experience: 3-7 years in ML/AI engineering or DevOps

Location: Bangalore (Whitefield) - Work from office, 5 days a week

Notice Period: Immediate joiners preferred (0-15 days)

Interview Mode: In-person interviews (Whitefield, Bangalore)

Education: B.E./B.Tech in CS, Software Engineering or Data Science

Please note: An online assessment is mandatory as part of the selection process.

Key Responsibilities

- Build end-to-end pipelines - data ingestion, training, evaluation, packaging, versioning and deployment
- Develop and maintain Jenkins CI/CD for Dev to QA to Production promotion with multi-stage gates
- Deploy model-serving APIs on AKS using FastAPI and vLLM; apply ONNX/TensorRT optimizations
- Set up observability - drift detection (Evidently AI), Prometheus/Grafana, Azure Monitor
- Apply DevSecOps practices - Key Vault, Managed Identity, SonarQube, Trivy/Snyk




- Build REST and WebSocket applications using FastAPI

Must-Have Skills

- 3+ years in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience
- CI/CD tooling: UV, Ruff, Pyrefly, Jenkins (strong), Azure DevOps, GitOps concepts; Git and pre-commit workflows
- Databricks ML pipelines (Delta Lake, Workflows, MLflow), Asset Bundles and PySpark
- Model serving: FastAPI, Docker, AKS; exposure to vLLM and ONNX/TensorRT optimization
- Python (robust - FastAPI, Pydantic, async), Bash, YAML/SQL scripting
- Cloud knowledge - Azure/AWS/GCP storage and AI related services
- Databases and storage: PostgreSQL, Redis, ADLS Gen2
- Understanding of containerization, Helm and infrastructure automation

Nice 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 BentoML exposure
- Groovy (Jenkinsfile)
- Manufacturing or semiconductor domain experience

📌 MLOps+ AI Engineer (Bengaluru)
🏢 RoundCircle
📍 Bengaluru

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