Position Title : Lead JAVA AI Software Engineer
Job Location: Chicago, IL
Work Model: Hybrid
Job Duration: 12 Months
Required Education/Experience:
Bachelor's and/or Master’s Degree in computer science, computer engineering or relevant field required w/ 10+years experience required.
• Building Location: Peoria, IL and/or Chicago office ( Chicago preferred)
• Remote Possibility: Hybrid; must go in office 2 days a week
• Daily Schedule: Monday thru Friday; 1st shift
Job Description
We are seeking a visionary Lead Software Engineer to architect and deliver next-generation software platforms driven by Artificial Intelligence and Agentic workflows. In this senior leadership role, you will bridge the gap between core full-stack application development and cutting-edge Large Language Model (LLM) integration. You will work with several high-performing engineering teams, championing the adoption of AI coding assistants to radically accelerate our internal developer velocity while designing reliable, production-grade AI systems for our customers.
Position’s Contributions to Work Group: Technical Architecture & AI Integration
Design and orchestrate multi-agent workflows and stateful AI systems using modern frameworks like LangChain, LangGraph, AutoGen, or CrewAI.
Integrate foundation models (e.g., Anthropic Claude, OpenAI GPT, Google Gemini) via secure API gateways, managing token limits, costs, and rate-limiting.
Architect data models optimized for AI-native structures, integrating relational databases with vector databases for semantic search and Retrieval-Augmented Generation (RAG).
Implement robust white-box testing and continuous evaluation metrics to monitor AI outputs for factual correctness, hallucinations, and safety compliance.
Champion secure, reliable, and scalable software solutions through architectural leadership, risk management, and continuous improvement of engineering practices.
Core Software Engineering:
Backend Systems: Deep proficiency in backend environments, microservices architectures, and API design using Java.
Cloud & DevOps: Extensive experience with containerization (Docker, Kubernetes) and public cloud infrastructure like AWS AI & LLM Infrastructure
Orchestration & Tooling: Hands-on experience with production-level AI agents, prompt engineering, and advanced Agentic PDLC patterns.
Vector Databases: Practical knowledge of vector technologies such as Pinecone, Milvus, Chroma, or pgvector.
AI Tool Fluency: Demonstrated mastery of AI coding assistants and a firm understanding of Model Context Protocol (MCP) to mediate agent connections with external databases.
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