IT-Software Engineer

Job Title: Senior Software Engineer / Technical Lead Generative AI & Agentic Systems

Location: Chicago, IL

Work Model: Hybrid

Work Auth: All Visas accepted (No H1 and No Fake Profiles)

Duration: 12 Months

Openings: 3

Shift: 9:00 AM 5:00 PM

Job Summary

We are seeking an experienced Senior Software Engineer / Technical Lead with strong expertise in Java, AWS, microservices, APIs, Generative AI, LLMs, RAG, and Agentic AI systems.

This role will focus on architecting and developing scalable AI-powered applications, multi-agent workflows, distributed backend systems, and cloud-native solutions. The ideal candidate will provide hands-on technical leadership, establish engineering standards, mentor developers, and work across multiple teams to deliver secure, reliable, and production-ready AI solutions.

Required Experience

• 8+ years of professional software engineering experience
• 2+ years in a technical leadership / architecture role
• Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field
• Strong experience designing and delivering large-scale distributed systems
• Proven experience leading architectural designs and cross-team technical initiatives

Required Technical Skills

• Strong Java backend development experience
• Microservices architecture
• REST APIs and enterprise API design
• AWS cloud services
• Docker and Kubernetes
• Cloud-native application architecture
• Generative AI / LLM-powered applications
• Retrieval-Augmented Generation (RAG)
• AI Agents / Agentic AI
• Prompt engineering
• Vector databases
• Software architecture and system design
• Application and cloud security
• CI/CD and modern engineering practices

Generative AI / Agentic AI Experience

• Design and develop multi-agent workflows and stateful AI systems
• Hands-on experience with frameworks such as:
  • LangChain
  • LangGraph
  • AutoGen
  • CrewAI
• Integrate foundation models such as:
  • OpenAI GPT
  • Anthropic Claude
  • Google Gemini
• Design secure API-based LLM integrations
• Manage token usage, API costs, rate limits, and model access
• Develop production-level AI agents and Agentic AI workflows
• Establish AI output evaluation for:
  • Factual accuracy
  • Hallucination detection
  • Safety and compliance
• Experience with Model Context Protocol (MCP) and external tool/data integrations

RAG & Data Architecture

• Design and implement Retrieval-Augmented Generation (RAG) solutions
• Integrate relational databases with vector databases
• Experience with vector technologies such as:
  • Pinecone
  • Milvus
  • Chroma
  • pgvector
• Design AI-native data models for semantic search and retrieval
• Strong understanding of data security and access controls for AI applications

Technical Leadership Responsibilities

• Lead architecture and design for enterprise-scale software and AI solutions
• Establish engineering standards and architectural governance
• Mentor software engineers and technical leads
• Conduct architecture reviews, technical discussions, and RFC reviews
• Influence engineering decisions across multiple development teams
• Identify and address technical debt, scalability, reliability, and performance concerns
• Drive secure coding, continuous integration, and cloud-native deployment practices
• Support modernization of existing systems, APIs, services, and data models

AI-Assisted Development

Strong hands-on experience with modern AI development tools such as:

• Cursor
• Claude Code
• Windsurf
• GitHub Copilot or similar AI coding assistants

The candidate will help integrate AI-assisted development into the SDLC while establishing governance around:

• Sensitive data handling
• Security guardrails
• Licensing and compliance
• Responsible AI usage
• Developer productivity

Additional Responsibilities

• Participate actively in Agile/Scrum ceremonies including:
  • Daily Standups
  • Sprint Planning
  • Grooming
  • Retrospectives
  • Demos
• Perform PR reviews and technical design reviews
• Collaborate with Product Owners, Architects, Engineering Managers, and Technical Leads
• Drive automation to improve engineering productivity and software quality
• Promote self-service engineering and platform enablement
• Troubleshoot complex application and infrastructure issues
• Champion continuous improvement across engineering teams