AI Ml Ops Engineer

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 (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.