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Jones Lang LaSalle

Advises on and manages commercial real estate

Forward Deploy Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 15 years of software or infrastructure development experience.
  • 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), using tools such as Claude Code, Codex, or equivalent assistants for day-to-day coding, documentation, and problem-solving.
  • Direct experience with AWS Agent Core or an equivalent platform, building, deploying, and operating agents in production.
  • Working knowledge of Model Context Protocol, 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 a cloud platform such as AWS, Azure, or GCP.
  • Ability to earn trust quickly, navigate ambiguity, and drive alignment across diverse teams.
  • Ability to work in fast-paced environments with shifting priorities and high ownership expectations.
  • Must be authorized to work in the United States without sponsorship.

Responsibilities

  • Lead solution design for complex, cross-functional data and AI problems, from initial discovery through technical blueprint.
  • Define and communicate architecture decisions, trade-offs, and delivery approaches to technical and non-technical audiences.
  • Design scalable, modular systems that balance speed with enterprise standards for reliability, security, and maintainability.
  • Participate in architecture reviews to ensure alignment with enterprise patterns and platform standards.
  • Create technical documentation, including architecture diagrams, data flow maps, API contracts, and solution briefs.
  • 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.
  • Deliver prototypes that balance speed with enterprise standards and are production-ready rather than throwaway.
  • Iterate on solutions based on direct feedback from product managers, program leads, and end users.
  • Develop front-end interfaces and dashboards that make data and AI outputs accessible to business users.
  • Apply user experience principles to simplify complex flows and make agent outputs accessible and actionable for non-technical stakeholders.
  • Design, build, and deploy AI agents and multi-agent systems to automate complex workflows end-to-end.
  • Develop and maintain reusable agent skills that compose into larger agentic pipelines.
  • Implement and extend Model Context Protocol 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 agentic AI techniques and tooling and introduce relevant advances to the team.
  • Integrate large language models, retrieval-augmented generation systems, and machine-learning models into production workflows.
  • Embed directly with product, program, and engineering teams to define problems and deliver solutions collaboratively.
  • Influence technical direction and build alignment across teams without relying on formal authority.
  • Communicate technical concepts to non-technical business stakeholders in writing, meetings, and executive presentations.
  • Mentor junior engineers and share practices for agentic development, prompt design, and rapid delivery.

Desired Qualifications

  • Experience with retrieval-augmented generation architectures, vector databases such as Pinecone, Weaviate, or pgvector, and semantic search.
  • Familiarity with prompt engineering, fine-tuning, and large language model evaluation techniques.
  • Experience with agent observability and tracing tools such as LangSmith, Arize, Weights & Biases, or similar tools.
  • Experience with containerization and continuous integration/continuous delivery practices, including Docker, Kubernetes, or GitHub Actions.
  • Background in real estate, financial services, or other data-intensive enterprise domains.
  • Experience facilitating technical discovery workshops, design sprints, or architecture reviews.

About the Company

Jones Lang LaSalle

Advises on and manages commercial real estate

Jones Lang LaSalle, known as JLL, advises property owners, occupiers and investors on commercial real estate worldwide. Its teams broker leases and property sales, arrange financing, value assets, manage buildings and workplaces, and oversee construction projects for businesses, institutions and governments. LaSalle Investment Management invests client capital in private property and real estate securities, while JLL Technologies develops workplace software and data tools. The company earns commissions, service fees, investment management fees and software revenue, with some client operating costs reimbursed through contracts. JLL took its current form through a 1999 merger and is headquartered in Chicago, Illinois.

Company size
10,001+
Company stage
IPO
Headquarters
Chicago, IL
Founded
1999

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