MLOps Engineer
First seen by Alion on Sep 29, 2026.
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
MLOps, Python, CI/CD, Jenkins, Databricks, Docker, Kubernetes, LLM, Cloud Architect - AI/ML, AIOps, DevOps