AI Engineer ML Ops

About the job MLOps Engineer / Developer Snapshot Experience: 4–6 years in ML/AI engineering or DevOps Reports To: MLOps Technical Lead / Manager, AIML Education: B.E./B.Tech in CS, Software Engineering, or Data Science 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. Skills: azure,ml,data science,fastapi,pipelines