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 (solid), 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.

📌 ML/AI Engineer (Bengaluru)
🏢 GyanSys
📍 Bengaluru