Lead Software Engineer (AI & Cloud Architecture)

Role: Lead Software Engineer – AI & Cloud Architecture
Location: Peoria, IL and/or Chicago, IL (Chicago preferred)
Work Model: Hybrid – 2 days per week onsite

Job Description

We are seeking a Lead Software Engineer to architect and deliver next-generation software platforms powered by Artificial Intelligence, Large Language Models (LLMs), and Agentic workflows.

This senior technical leadership role will bridge enterprise backend engineering, software architecture, cloud-native development, and AI/LLM integration. The engineer will work across multiple high-performing engineering teams, driving the adoption of AI-assisted development tools while designing secure, scalable, and production-ready AI systems.

The ideal candidate will have strong experience in Java backend development, distributed systems, microservices, APIs, AWS, cloud-native technologies, AI agents, LLM applications, and software architecture, along with the ability to influence technical decisions across multiple teams.

Key Responsibilities

AI Systems & Technical Architecture

• Design and orchestrate multi-agent workflows and stateful AI systems using frameworks such as LangChain, LangGraph, AutoGen, or CrewAI.
• Integrate foundation models such as Anthropic Claude, OpenAI GPT, and Google Gemini through secure APIs and gateways.
• Design solutions for token management, rate limiting, cost optimization, security, and scalability.
• Architect AI-native data models integrating relational databases with vector databases for semantic search and Retrieval-Augmented Generation (RAG).
• Implement testing and continuous evaluation frameworks to monitor AI outputs for accuracy, hallucinations, safety, and reliability.
• Lead architectural decisions for secure, scalable, production-grade AI and software platforms.

Backend Engineering & Cloud

• Design and develop backend systems, microservices, APIs, and distributed applications using Java.
• Drive cloud-native architecture and deployment using AWS, Docker, and Kubernetes.
• Evaluate existing services, APIs, systems, and data models for opportunities involving modernization, scalability, performance, and reliability.
• Establish engineering standards for secure coding, CI/CD, cloud-native deployment, and software quality.

AI-Assisted Developer Experience

• Drive adoption of AI-assisted development tools such as Cursor, Claude Code, and Windsurf.
• Establish responsible AI development practices covering data sensitivity, licensing, security, and governance.
• Promote self-service engineering capabilities and platform enablement.
• Identify and implement automation opportunities that improve developer productivity, engineering velocity, and software quality.

Technical Leadership & Collaboration

• Mentor engineers and technical leaders on software architecture, system design, AI engineering, and engineering excellence.
• Lead architecture reviews, RFCs, technical discussions, and engineering forums.
• Collaborate with Product Owners, Technical Leads, Architects, Engineering Managers, and Scrum teams.
• Influence technical decisions across multiple engineering teams and drive organization-wide technical initiatives.
• Address technical debt, engineering health, modernization, AI adoption, and continuous improvement.

Typical Day / Week

• Participate in Daily Standups, Sprint Planning, Backlog Grooming, Retrospectives, and Sprint Demos.
• Conduct code and pull-request reviews.
• Collaborate with Scrum teams on architecture, design, implementation, and technical problem-solving.
• Lead technical discussions and architecture reviews across teams.
• Mentor engineers and provide technical guidance.
• Evaluate new AI technologies, developer productivity tools, and architectural patterns.
• Drive implementation of AI-enabled engineering and automation initiatives.
• Work closely with cross-functional stakeholders to develop scalable solutions.

Work Environment

Highly collaborative environment requiring significant interaction with Scrum teams and other engineering organizations.

Required Qualifications

Education & Experience

• Bachelor’s degree in Computer Science, Electrical Engineering, or a related field plus 10+ years of experience.
• Master’s degree plus 8+ years of experience.
• 8+ years of professional software engineering experience.
• 2+ years of technical leadership experience.

Core Software Engineering

• Strong backend engineering experience with Java.
• Deep experience with microservices, APIs, distributed systems, and software architecture.
• Proven experience designing and delivering large-scale distributed systems.
• Strong experience with Docker, Kubernetes, and AWS.
• Experience developing and deploying cloud-native applications.

AI / LLM / Agentic Systems

• Hands-on experience with Generative AI, LLM-powered applications, AI agents, or intelligent automation.
• Experience designing production-level AI agents and Agentic workflows.
• Strong knowledge of prompt engineering and Agentic Development Lifecycle (PDLC) patterns.
• Experience with AI orchestration frameworks such as LangChain, LangGraph, AutoGen, or CrewAI.
• Experience with vector databases such as Pinecone, Milvus, Chroma, or pgvector.
• Experience with RAG and semantic search.
• Strong familiarity with AI coding assistants and developer productivity platforms.
• Understanding of Model Context Protocol (MCP) for connecting AI agents with external systems and databases.

Architecture & Security

• Strong understanding of application security, cloud security, and secure software architecture.
• Experience establishing technical governance, engineering standards, and architectural best practices.
• Ability to identify and manage technical risks while driving scalable engineering solutions.

Leadership & Communication

• Experience influencing multiple engineering teams.
• Strong communication, collaboration, and stakeholder management skills.
• Demonstrated ability to mentor engineers and technical leaders.
• Ability to lead architectural discussions and develop solutions across organizational boundaries.
• Ability to work effectively under pressure and within time constraints.
• Passion for technology and a strong team-oriented mindset.