Staff Forward Deployed Engineer

JLL•Westmont, CO

20h•Onsite

About The Position

As a Staff Forward Deployed Engineer, you will operate at the front lines of innovation — embedded directly with multiple cross-functional teams to rapidly design, architect, prototype, and deploy solutions that solve their highest-priority 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 will act as the embedded technical owner across several teams simultaneously — diagnosing their workflow and data problems, then architecting and building the fix yourself. You will use modern AI coding assistants day-to-day to design and implement solutions, and you're expected to be fluent extending that toolset — building custom plugins/skills, redesigning data pipelines and data flows, and deploying agents to production via AWS Bedrock AgentCore. At the Staff level, your impact extends beyond any single team: you define the patterns and playbooks other engineers use to deliver, and you act as the escalation point for the hardest technical and stakeholder problems across the portfolio. You bring an exceptional combination of skills: deep hands-on engineering experience across AI agents, data architecture, and full-stack development, and the interpersonal skills to earn trust, drive alignment, and influence without authority across multiple teams at once. You are equally comfortable whiteboarding a system architecture with senior stakeholders and shipping production code the same afternoon.

Requirements

8–12+ years of software engineering experience, with substantial time in customer-facing or embedded technical delivery roles
Strong software architecture background — able to independently design and defend system architecture across varied tools and platforms, not tied to one stack
Direct hands-on experience building and deploying AI agents on AWS Bedrock AgentCore — required
Experience with Model Context Protocol (MCP) server/client implementation — required
Data engineering experience — building and redesigning data pipelines and data flows across varied sources
Full-stack development proficiency — backend services, APIs, and front-end interfaces/dashboards
Demonstrated experience mentoring engineers and defining technical standards adopted beyond your own work
Excellent written and verbal communication skills; able to manage multiple concurrent internal stakeholder relationships
Candidates must be authorized to work in the United States without sponsorship.

Nice To Haves

Direct hands-on experience using AI coding assistants (e.g., Claude, Cursor, GitHub Copilot) as a primary development workflow; experience building custom plugins/skills for these tools is a strong plus

Responsibilities

Lead solution design for complex, cross-functional data and AI problems — from initial discovery through to technical blueprint
Evaluate and select the right tools and architecture for each problem — no single stack applies across every team you support, so you must be able to reason across different platforms, data sources, and integration patterns
Define and communicate architecture decisions, trade-offs, and delivery approaches to both technical and non-technical audiences
Design scalable, modular systems that balance speed with enterprise standards for reliability, security, and maintainability
Create clear technical documentation: architecture diagrams, data flow maps, API contracts, and solution briefs
Establish reusable architecture patterns and reference designs that other engineers adopt across engagements
Use AI coding assistants as your primary development environment for day-to-day solution building — from prototyping through production implementation
Build and maintain custom plugins/skills for your AI coding assistant that package reusable capabilities for recurring problems across teams
Redesign and rebuild data pipelines and data flows to unblock use cases — including source integration, transformation logic, and downstream delivery
Deploy and operate AI agents in production via AWS Bedrock AgentCore
Translate ambiguous business requirements from stakeholders into concrete technical solutions with minimal hand-holding
Balance speed of delivery with enterprise standards — your prototypes are production-ready, not throwaway
Develop intuitive front-end interfaces and dashboards that bring data and AI outputs to life for business users
Design, build, and deploy AI agents and multi-agent systems that automate complex workflows end-to-end
Develop and maintain agent skills and plugins — discrete, reusable capabilities that compose into larger agentic pipelines
Implement and extend Model Context Protocol (MCP) servers and clients to connect agents with enterprise tools, APIs, and data sources
Design evaluation harnesses, guardrails, and monitoring pipelines to ensure agent reliability and safety in production on AgentCore
Stay current with the rapidly evolving agentic AI landscape (AI coding assistants, AgentCore, MCP ecosystem) and proactively bring new techniques to the practice
Define and evolve delivery practices and playbooks used across multiple cross-functional teams
Serve as the technical escalation point for the most ambiguous or high-stakes problems across the portfolio
Provide direct input to the hiring manager on recurring patterns and roadmap priorities based on field signal
Mentor and elevate other engineers on AI-assisted agentic development, plugin design, and rapid delivery
Embed directly with multiple cross-functional teams simultaneously 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 presentations
Foster a collaborative, low-ego culture where speed and quality go hand in hand