AI Engineer

Job DescriptionJob DescriptionClient: Leading Healthcare clientLocation: Schaumburg, IL- Hybrid (3Days/week)Duration: 24+ MonthsWhat You'll Do• Design and ship production GenAI applications: RAG pipelines, LLM-based services, and agentic workflows using tools, memory, and multi-step reasoning• Build and operate MLOps/LLMOps pipelines — CI/CT/CD for model testing, validation, versioning, and promotion across environments• Stand up LLM application infrastructure: gateways, orchestration layers, model routing, caching, and cost/latency optimization• Deploy scalable, cloud-native ML services on Azure using Docker and Kubernetes• Implement model monitoring for drift, latency, bias, and data quality, with automated retraining• Ensure governance and compliance — lineage, reproducibility, observability, and responsible AI controls for PHI-adjacent systems• Partner with data scientists, data engineers, and software engineers to define production AI standardsRequired Qualifications• 5+ years in machine learning or AI engineering, including production systems• Strong Python with experience building maintainable, testable production services• 3+ years building Generative AI / LLM applications — prompt engineering, fine-tuning, and LLM API integration• Hands-on experience with RAG architectures and an LLM framework such as LangChain or LangGraph• Production deployment experience on a major cloud (Azure preferred) with Docker, CI/CD, and containerized workflowsPreferred Qualifications• Agentic AI in production — orchestration, tool use, memory, and multi-step reasoning workflows• ML framework depth with PyTorch, TensorFlow, or Hugging Face, including model fine-tuning• Azure-first depth: Azure Machine Learning (workspaces, compute, pipelines, jobs, endpoints) and Azure Databricks• Kubernetes and ML lifecycle platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI• Infrastructure-as-code with Terraform and pipelines in GitHub Actions• Streaming and data infrastructure: Spark, Kafka, MS SQL Server, NoSQL (Cassandra)• LLM observability tooling such as LangFuse• Proven experience optimizing LLM latency, cost, and performance at scale• Experience in regulated environments — healthcare / HIPAA, PHI handling, and responsible AI governance.