IT-Software Engineer

Job Title: IT-Software Engineer
Location : Chicago, IL
Type: W2 Contract




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.