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 (robust), 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 (solid — 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 (India)
🏢 GyanSys
📍 India