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

Must-Have Skills :

- 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 (strong - 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.