Forward Deployed Engineer

JLL; Chicago (IL); On-site; USD 280,000 - 300,000; Full time

Job summary

JLL is seeking a Forward Deployed Engineer to operate at the front lines of innovation, prototyping and deploying enterprise-grade AI-driven solutions in days, not months. You will work directly with product and engineering teams to design, architect, and deliver complex, data-centered systems and intuitive dashboards for business users.

The role demands hands-on AI agent development, multi-agent orchestration, and experience with platforms like AWS Agent Core, MCP, and LangChain.

Qualifications

• 8+ years of professional software engineering, with architecture ownership.
• Hands-on AI agents development and end-to-end system design.
• Experience with AWS Agent Core or similar and MCP servers/clients.
• Proficiency in Python and a modern frontend framework; cloud familiarity.
• Strong communication to align stakeholders and drive decisions.

Responsibilities

• Lead solution design for cross-functional data and AI problems from discovery to blueprint.
• Define architecture decisions and communicate trade-offs to technical and non-technical audiences.
• Design scalable systems balancing speed with reliability, security, and maintainability.
• Develop working prototypes in compressed timeframes and production-ready code.
• Build AI agents, agent skills, and integrate with enterprise tools and data sources.

Skills

AI agents

Full-stack dev

Python

Frontend (React/Vue/Angular)

AWS Agent Core

LangChain

MCP

CI/CD

Tools

Claude Code

Codex

LangChain

Docker

Kubernetes

GitHub Actions

Job description

Role Overview

As a Forward Deployed Engineer, you will operate at the front lines of innovation — embedded directly with subject matter experts, product, and engineering teams to rapidly design, architect, prototype, and deploy solutions that solve high-priority business problems. This is not a traditional engineering role with long delivery cycles. You will be expected to go from concept to working prototype in days, not months, while maintaining enterprise-grade standards for quality, security, and scalability.

You bring an exceptional combination of skills: deep hands-on engineering experience across AI agents and full-stack development, and the interpersonal skills to earn trust, drive alignment, and influence without authority. You are equally comfortable whiteboarding a system architecture with senior stakeholders and writing production code the same afternoon. You have direct experience developing agents on platforms like AWS Agent Core, and you thrive in fast, ambiguous environments where creative thinking and execution speed matter.

Key Responsibilities

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

Qualifications

Critical skills

• 8+ years of professional software engineering experience, including solution design and architecture ownership
• Demonstrated ability to architect end-to-end systems — from requirements through deployment — with clear documentation and stakeholder communication
• Hands-on experience building AI agents, including defining agent skills, tool use, memory, and multi-step reasoning
• Experience with AI-Augmented Engineering (Harness Engineering) — actively use tools like Claude Code, Codex, or equivalent assistants to accelerate coding, documentation, and problem-solving day-to-day.
• Direct experience with AWS Agent Core or equivalent — building, deploying, and operating agents in production
• Working knowledge of Model Context Protocol (MCP) — including building or consuming MCP servers to connect agents with external systems
• Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel
• Proficiency in Python and at least one front-end framework (React, Vue.js, or Angular)
• Experience with cloud platforms (AWS, Azure, or GCP)
• Exceptional communication and interpersonal skills — you can earn trust quickly, navigate ambiguity, and drive alignment across diverse teams
• Comfort working in fast-paced environments with shifting priorities and high ownership expectations

Desirable skills

• Experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector), and semantic search
• Familiarity with prompt engineering, fine-tuning, and LLM evaluation techniques
• Experience with agent observability and tracing tools (LangSmith, Arize, Weights & Biases, or similar)
• Experience with containerization and CI/CD practices (Docker, Kubernetes, GitHub Actions)
• Background in real estate, financial services, or other data-intensive enterprise domains
• Experience facilitating technical discovery workshops, design sprints, or architecture reviews

This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship.

Location & Compensation

Estimated compensation for this position: 280,000.00 – 300,000.00 USD per year.

Location

On-site – Chicago, IL, San Francisco, CA

Notes

Accepting applications on an ongoing basis until candidate identified.

Privacy and Equal Opportunity

JLL is an Equal Opportunity Employer. See our Privacy Notice and candidate privacy statements on the career site for details. We comply with applicable laws and provide accommodations as needed during the employment process. If you need an accommodation, please email [e-mail].