Lead Software Engineer (AI & Cloud Architecture)

The AES Group•Chicago, IL

2d•Hybrid

About The Position

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.

Requirements

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.
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.
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.
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.
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.

Responsibilities

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.
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.
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.
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.
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.