Forward Deployed Engineer

About JLL Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward.

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