MLOps Engineer - AI Deployment
First seen by Alion on Sep 16, 2026.
About the Role:
We are looking for a hands-on MLOps Engineer specializing in ML engineering, model deployment, model governance, and observability. The role covers the complete lifecycle of Deep Learning models, LLMs, and SLMs.
Key Responsibilities:
- Build and manage MLOps and LLMOps pipelines.
- Deploy, host, and scale Deep Learning models, LLMs, and SLMs.
- Manage model versioning, deployment, rollout, rollback, and retirement.
- Host models on Databricks, Kubernetes, OpenShift, and GPU infrastructure.
- Implement model governance, lineage, approval workflows, and compliance controls.
- Build monitoring, tracing, logging, and drift-detection capabilities.
- Optimize model latency, throughput, GPU utilization, and cost.
- Support cloud, on-premises, hybrid, and air-gapped environments.
Required Skills:
- 3 - 5 years in MLOps, LLMOps, ML Engineering, or AI Engineering.
- Strong Python.
- Hands-on Databricks and/or Azure ML.
- Experience with Deep Learning, LLMs, SLMs, RAG, and Hugging Face.
- Experience deploying PyTorch and TensorFlow models.
- Strong Kubernetes, Databricks, and GPU deployment experience.
- Experience with vLLM, Triton Inference Server, Ray Serve, SGLang, or Databricks Model Serving.
- Strong NVIDIA GPU and CUDA knowledge.
- Model Registry, Governance, Monitoring, Drift Detection, and AI Observability.
- SQL Server, PostgreSQL, Oracle, MySQL, or MongoDB.
- Vector databases such as Pinecone, Chroma, FAISS, Milvus, or Azure AI Search.
- REST APIs, WebSockets, and Streaming HTTP.
- MLflow, OpenTelemetry, LangFuse, Splunk, and Grafana/ELK.
- Jenkins and Azure DevOps.
- Keycloak/authentication setup experience.
Skills
MLOps, Python, Kubernetes, Databricks, Deep Learning, PyTorch, Tensorflow, Grafana, Artificial Intelligence