Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

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.