Rag Developer Jobs in Illinois (NOW HIRING)

Job Summary
We are looking for an experienced AI Engineering / Generative AI (GenAI) Lead to lead the design and development of production-grade enterprise AI solutions, with a strong focus on Agentic AI, AI orchestration, Azure, Snowflake, RAG, and LLM/ML integration.
Key Responsibilities
• Lead the design and development of production-grade enterprise Generative AI and AI Engineering solutions.
• Design and implement AI orchestration layers, agent workflows, and intelligent workflow automation.
• Develop and integrate Agentic AI and autonomous AI systems using frameworks such as LangChain, LangGraph, Microsoft Semantic Kernel, or AutoGen.
• Design and implement Retrieval-Augmented Generation (RAG) solutions and document intelligence applications.
• Integrate LLMs with ML models, enterprise data, APIs, and business workflows.
• Develop AI-enabled applications, APIs, REST services, and microservices.
• Build and deploy AI solutions using Microsoft Azure AI services, including Azure OpenAI, Azure Machine Learning, Azure Functions, and Azure Kubernetes Service (AKS).
• Develop and implement AI solutions using Snowflake, including Semantic Views and Cortex Analyst.
• Design and implement vector database and vector store solutions using embeddings, semantic search, and retrieval technologies.
• Establish and implement MLOps and LLMOps practices for production deployment, monitoring, evaluation, and lifecycle management.
• Implement prompt, LLM, and agent evaluation frameworks to assess quality, accuracy, groundedness, safety, and performance.
• Implement AI guardrails, governance, security, and Responsible AI practices.
• Collaborate with technical and business stakeholders to define AI strategies, architecture, and enterprise solutions.
• Provide technical leadership and guidance across AI engineering initiatives.
Required Qualifications
• Strong experience in Generative AI, GenAI, AI Engineering leadership, and enterprise AI solution development.
• Strong hands-on experience with Microsoft Azure AI, including Azure OpenAI, Azure Machine Learning (Azure ML), Azure Functions, and Azure Kubernetes Service (AKS).
• Strong experience with Snowflake, including Semantic Views and Cortex Analyst.
• Strong experience with Agentic AI and AI agent frameworks such as LangChain, LangGraph, Microsoft Semantic Kernel, or AutoGen.
• Strong experience designing and implementing RAG solutions and document intelligence applications.
• Strong understanding of Machine Learning (ML), MLOps, and LLMOps, including production deployment, monitoring, evaluation, and lifecycle management.
• Strong Python development experience for AI/ML applications and services.
• Experience developing APIs, REST services, microservices, and AI-enabled applications.
• Experience building AI orchestration layers, agent workflows, and intelligent workflow automation.
• Experience integrating LLMs with ML models, enterprise data, APIs, and business workflows.
• Experience with vector databases/vector stores, embeddings, semantic search, and retrieval technologies.
• Experience implementing AI guardrails, AI governance, security, and Responsible AI practices.
• Experience with prompt, LLM, and agent evaluation frameworks to assess quality, accuracy, groundedness, safety, and performance.
• Strong enterprise architecture, technical leadership, stakeholder management, and communication skills.
Preferred Qualifications
• Experience developing AI-powered insight generation solutions.
• Experience designing production-grade AI engineering platforms.
• Experience working across the Azure and Snowflake AI ecosystem.
• Experience with autonomous AI systems and advanced agent orchestration.
Technical Skills
• Generative AI / GenAI
• AI Engineering
• Agentic AI
• AI Agents
• Large Language Models (LLMs)
• Azure OpenAI
• Azure Machine Learning (Azure ML)
• Azure Functions
• Azure Kubernetes Service (AKS)
• Snowflake
• Cortex Analyst
• Semantic Views
• LangChain
• LangGraph
• Microsoft Semantic Kernel
• AutoGen
• Retrieval-Augmented Generation (RAG)
• Document Intelligence
• Machine Learning (ML)
• MLOps
• LLMOps
• Python
• APIs and REST Services
• Microservices
• AI Orchestration
• Workflow Automation
• Vector Databases
• Embeddings
• Semantic Search
• AI Guardrails
• AI Governance
• Responsible AI
• Prompt Evaluation
• LLM Evaluation
• Agent Evaluation
• Enterprise Architecture