Lead AI Software Engineer

Role: Lead AI Software Engineer (507554)

Location: Chicago, IL (Hybrid – 2 Days a week onsite)

Duration: 12 Month Contract

Must Have Skills/Attributes: AI, API, AWS, Java, LLM (Large Language Model), Microservices, RAG (Retrieval-Augmented Generation)

Experience Desired:

· Java Backend Development

· Microservices & API Architecture (10+ Years)

· Software Architecture & Large-Scale Distributed Systems (8+ Years)

· AWS Cloud, Docker & Kubernetes (Cloud-Native Development) (8+ Years)

· Generative AI, LLMs, AI Agents, Prompt Engineering & RAG (LangChain/LangGraph/AutoGen/CrewAI) (3+ Years)

· Technical Leadership, Engineering Governance & Mentoring Multiple Teams (2+ Years)

Required Minimum Education: Bachelor’s Degree

Job Description

Required Education/Experience:

· Bachelor’s degree in Computer science or Electrical engineering or related field

Required Technical Skills:

· 8+ years of professional software engineering experience and 2+ years in a technical leadership role.

· Proven experience designing and delivering large-scale distributed systems.

· Strong expertise in software architecture, APIs, microservices, cloud-native applications, and modern engineering practices.

· Experience influencing multiple engineering teams and leading organization-wide technical initiatives.

· Strong communication, collaboration, and stakeholder management skills.

· Demonstrated ability to mentor engineers and drive technical excellence.

· Experience with Generative AI, AI agents, LLM-powered applications, or intelligent automation platforms.

· Experience with AI development tools, coding assistants, and modern developer productivity platforms.

· Knowledge of security architecture, application security, and cloud security best practices.

· Experience establishing technical governance and engineering standards across multiple teams.

Required Soft Skills:

· Ability to work under pressure and within time constraints

· Passion for technology and an eagerness to contribute to a team-oriented environment

· Lead architectural designs and able to work across teams to come up with solutions.

Summary of Role:

· 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.

Technical Leadership & Workflow Optimization:

· Mentor and scale an engineering team, establishing engineering standards, best practices and architectural governance for secure coding, continuous integration, and cloud-native deployment.

· Drive AI-assisted engineering velocity by integrating next-gen development tools (e.g., Cursor, Claude Code, Windsurf) into the team's daily Software Development Life Cycle (SDLC).

· Establish governance policies and coach developers on the responsible use of AI, focusing on input data sensitivity, license compliance, and security guardrails.

· Evaluate existing systems, services, APIs, and data models to identify opportunities for modernization, scalability, and performance improvements.

Influence Across Teams & Champion Developer Experience:

· Facilitate technical leadership forums with Product Owners, Technical Leads, Architects, and Engineering Managers.

· Drive conversations around architecture, engineering health, technical debt, AI adoption, and continuous improvement.

· Mentor engineers and technical leaders on system design, software architecture, and engineering excellence.

· Foster collaborative decision-making through architecture reviews, RFCs, and technical discussions.

· Promote self-service engineering capabilities and platform enablement.

· Drive automation initiatives that improve productivity and software quality.

Interaction with team:

· Significant interaction required with Scrum team. Interaction during all scrum ceremonies such as Daily Standups, Sprint Planning, Grooming, Retro, Demo etc

· Also need to interact with team for PR Reviews, also need to engage with other teams as needed.

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.