Job Summary

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.

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 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.

• Application Development – REST, WebSocket Frameworks using FastAPI

Skill Requirements

- 4+ years in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.
- CI/CD tooling: CI 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 for data processing.
- 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, 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.

📌 AI Ml Ops Engineer (Bengaluru)
🏢 HCLTech
📍 Bengaluru