Lead Forward Deployed Engineer

Lead Forward Deployed Engineer

Prologis

The Lead FDE partners with business teams to reimagine how work gets done with AI and leads the technical delivery of Prologis' most complex business-embedded AI deployments while remaining hands-on in architecture, code, evaluation and production implementation. As our first FDE hire, you will help establish this capability at Prologis and shape how we turn AI into new ways of working and measurable business results.

You will be deliberately assigned to a primary Prologis business domain. External hires will generally remain in that anchor domain for 9 to 12 months, and potentially longer, while leading several discrete deployments. This extended assignment allows deep technical expertise to be paired with Prologis operating context and business credibility without creating permanent embedded support.

You will structure ambiguous programs, set technical direction, guide Senior FDEs and partner engineers, resolve cross-functional blockers and create patterns that can be reused across Prologis. You will remain accountable for meeting agreed business acceptance criteria and supporting validation through production release, technical stabilization and ownership transfer. This is a senior individual-contributor position and does not require direct people management.

• Build deep fluency in an anchor domain and lead a portfolio of deployments spanning multiple workflows, business units or enterprise systems.
• Ensure each deployment has defined outcomes, sponsorship, architecture, milestones, production ownership and a handoff plan.
• Work directly with business leaders, frontline users and AI Product to identify high-value opportunities, understand how work is done today and challenge assumptions about how it could work with AI, informing the value case, success measures and operating constraints for each deployment.
• Translate strategic objectives into redesigned workflows that remove unnecessary steps and handoffs and combine AI agents, software and human judgment effectively; define the technical roadmaps, deployment architectures, dependencies and acceptance criteria needed to deliver them.
• Architect, build, test and troubleshoot production-grade AI applications, agents, retrieval systems, integrations and workflow solutions.
• Set technical direction for FDE deployments and guide implementation across Senior FDEs, central AI engineers and partner teams.
• Resolve tradeoffs among business value, engineering quality, security, speed, maintainability and cost.
• Design representative test sets, acceptance thresholds, regression testing, observability, security, privacy, auditability and human controls into the solution.
• Coordinate closely with central AI Product, Applied AI Engineering, Platform, Data and Knowledge, Security and Governance, Process and Agent Orchestration, Enablement, Architecture and AI Operations teams.
• Facilitate business validation against agreed acceptance criteria, resolve technical defects and partner with business owners and Enablement to address adoption barriers and validate workflow improvements during rollout and stabilization. Establish production-readiness and handoff criteria, and ensure solutions have durable ownership after deployment.
• Business owners retain accountability for value baselines, adoption, change management and value realization; the FDE partners with them on process redesign, adoption barriers and validation of workflow improvements.
• Identify patterns that should become shared capabilities, reusable components, evaluation assets or reference architectures.
• Mentor Senior FDEs and raise the quality of technical discovery, engineering, evaluation and stakeholder communication.
• Provide concise executive reporting on value, delivery progress, risks, tradeoffs and decisions.
• Help define the Prologis FDE delivery methodology, engineering standards and engagement playbooks.

• Complex deployments progress beyond demonstrations and pilots into stable production, meet agreed technical, delivery and business acceptance criteria, and demonstrate improvements in how work gets done, validated with business owners against agreed baselines.
• Multiple work streams remain aligned and critical dependencies are resolved early.
• Solutions are secure, reliable, observable, maintainable and supported by clear production ownership.
• Reusable patterns are scaled through the central AI organization rather than…