Forward Deployed Engineer

JLL (Jones Lang LaSalle)

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Francisco, CAChicago, IL
Salary
$187,100–$229,200 / yr
Employment
Full-time
Posted
15 days ago
Freshness
Confirmed live yesterday
Closes
Jul 30, 2027

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $185k
This role $208k
$130k $240k
below market most similar roles pay here above market

This role pays more than 67% of similar roles. Most pay $147,182–$222,812 — the blue band above. At the midpoint, this role pays about $208k versus about $185k for comparable roles.

Based on 240 similar postings.

Employer

About JLL (Jones Lang LaSalle)

JLL (Jones Lang LaSalle) is a global professional services firm specializing in real estate and investment management, providing services to buyers, sellers, tenants, landlords, investors, and developers. Industry: Commercial Real Estate Services

JLL (Jones Lang LaSalle) currently has 41 open roles on FindRole.

Listed pay typically runs $168,500–$205,000 across 28 roles with salary data.

Most-posted roles

View all roles at JLL (Jones Lang LaSalle)

At a glance

TL;DR · Forward Deployed Engineer

As a Forward Deployed Engineer, you will join a team to operate at the front lines of innovation, embedded with subject matter experts and product teams to rapidly design, architect, and deploy solutions for high-priority business problems. You will move from concept to working prototype in days, building scalable, modular systems that balance speed with enterprise standards for security and reliability. Your daily work involves developing AI agents and multi-agent systems, implementing Model Context Protocol servers, and integrating LLMs and RAG systems into production workflows. You will utilize Python, React, Vue.js, or Angular, alongside frameworks like LangChain, LangGraph, and AutoGen. The role requires deep hands-on experience with AWS Agent Core and cloud platforms to solve complex data and AI problems within a fast-paced, ambiguous environment.

What does a Forward Deployed Engineer earn?

Median $219575 from 46 postings across 19 companies.

See salary data

What you'll do

  • Lead solution design for complex data and AI problems from initial discovery through technical blueprinting.
  • Design scalable, modular systems that balance rapid development with enterprise standards for security and reliability.
  • Create clear technical documentation including architecture diagrams, data flow maps, and API contracts.
  • Deliver working prototypes for complex AI problems within compressed timeframes of days to weeks.
  • Build and deploy end-to-end AI agents and multi-agent systems to automate complex workflows.
  • Implement Model Context Protocol (MCP) servers to connect AI agents with enterprise tools and data sources.
  • Develop evaluation harnesses, guardrails, and monitoring pipelines to ensure agent reliability in production.
  • Create intuitive front-end interfaces and dashboards to make AI outputs accessible to non-technical stakeholders.

What we're looking for

  • 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.
  • Hands-on experience building AI agents, including defining agent skills, tool use, memory, and multi-step reasoning.
  • Experience with AI-Augmented Engineering tools like Claude Code or Codex to accelerate coding and problem-solving.
  • Direct experience with AWS Agent Core or equivalent for building and operating agents in production.
  • Working knowledge of Model Context Protocol (MCP) 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 like React, Vue.js, or Angular.
  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Experience with RAG architectures, vector databases, and semantic search (preferred).
  • Familiarity with prompt engineering, fine-tuning, and LLM evaluation techniques (preferred).
  • Experience with agent observability tools and containerization/CI/CD practices (preferred).

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