Job Title: Software Engineer – Agentic AI Specialist
Location: Chicago, IL (Onsite Locals only)
Duration: 6 + months
Client: Encora / JLL
Rate: $60/Hr
Job Description:
Solution Design & Architecture
· Lead solution design for complex, cross-functional data and AI problems — from initial discovery through to technical blueprint
· Define and communicate architecture decisions, trade-offs, and delivery approaches to both technical and non-technical audiences
· Design scalable, modular systems that balance the need for speed with enterprise standards for reliability, security, and maintainability
· Participate in architecture reviews, ensuring alignment with enterprise patterns and platform standards
· Create clear technical documentation: architecture diagrams, data flow maps, API contracts, and solution briefs
Rapid Prototyping & Solution Delivery
· Design and deliver working prototypes for complex data and AI problems within compressed timeframes, often days to weeks
· Translate ambiguous business requirements into concrete technical solutions with minimal hand-holding
· Balance speed of delivery with enterprise standards — your prototypes are production-ready, not throwaway
· Continuously iterate on solutions based on direct feedback from product managers, program leads, and end users
· Develop intuitive front-end interfaces and dashboards that bring data and AI outputs to life for business users
· Apply strong UX instincts to simplify complex flows and make agent outputs accessible and actionable for non-technical stakeholders
AI Agent Development
· Design, build, and deploy AI agents and multi-agent systems that automate complex workflows end-to-end
· Develop and maintain agent skills — discrete, reusable capabilities that compose into larger agentic pipelines
· Implement and extend Model Context Protocol (MCP) servers and clients to connect AI agents with enterprise tools, APIs, and data sources
· Design evaluation harnesses, guardrails, and monitoring pipelines to ensure agent reliability and safety in production
· Stay current with the rapidly evolving agentic AI landscape and proactively introduce new techniques and tooling to the team
· Integrate LLMs, RAG systems, and ML models into production workflows
Collaboration & Stakeholder Engagement
· Embed directly with product, program, and engineering teams to co-define problems and co-deliver solutions
· Influence technical direction and build alignment across teams without relying on formal authority
· Communicate complex technical concepts clearly to non-technical business stakeholders — in writing, in meetings, and in executive presentations
· Mentor and elevate junior engineers, sharing patterns and practices for agentic development, prompt design, and rapid delivery
· Foster a collaborative, low-ego team culture where speed and quality go hand in hand
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