Ford Pro Intelligence builds data and AI-powered products that help commercial customers manage fleets more efficiently, safely, and reliably. Our team works across connected vehicle data, fleet operations, driver insights, service planning, charging workflows, and business intelligence to create software that helps customers make better decisions and take action. You will design and develop LLM-powered applications that connect natural language experiences with enterprise data, cloud services, and operational workflows. You will work across agent orchestration, tool calling, prompt engineering, structured data grounding, evaluation, safety, observability, and production deployment.
This is a hands-on engineering role for someone who wants to build practical GenAI systems that solve real customer problems, not isolated prototypes.
In this role, you will:
Ship production GenAI features that improve customer workflows
Build reliable patterns for LLM integration, tool use, evaluation, and observability
Improve the accuracy, latency, cost, and trustworthiness of AI-enabled systems
Help teams move from prototypes to maintainable production services
Partner across engineering, product, data, security, and business teams
Contribute to responsible AI practices in an enterprise software environment
Responsibilities
Design and improve LLM-based systems that can interpret user intent, retrieve context, call tools, interact with APIs, and coordinate multi-step workflows.
Areas of work may include:
Agent orchestration
Tool calling and function calling
Context and conversation state management
Structured model outputs
Workflow automation
Human-in-the-loop review patterns
Integration with backend services and enterprise APIs
Develop systems that transform complex operational signals into concise summaries, recommendations, alerts, and decision-support experiences.
The goal is to help customers move from data to action faster.
Build the foundations needed to operate GenAI systems responsibly in production, including:
Evaluation datasets and test harnesses
Prompt and workflow regression testing
Groundedness and accuracy checks
Guardrails and safety controls
Prompt injection and misuse defenses
Latency, cost, and quality monitoring
Traceability for debugging AI behavior
Our environment is primarily built around Google Cloud Platform, with production services developed mainly in Python and Kotlin. You do not need experience with every tool listed below, but strong candidates should be comfortable learning across this environment.
Qualifications
4+ years of professional software engineering experience
3+ years of hands-on experience building production backend services, APIs, data products, or AI-enabled applications
3+ years in roles demonstrating strong programming skills in Python, Kotlin, or Java
2+ years practical experience using LLM APIs or building GenAI-enabled applications
2+ years in roles demonstrating familiarity with prompt engineering, RAG, tool calling, function calling, or structured model outputs
2+ years comfortable working years of experience with SQL and structured data
2+ years of experience in roles requiring a strong understanding of testing, monitoring, reliability, and maintainable software design
Preferred
1+ years of experience in roles demonstrating the ability to collaborate with product managers, engineers, data teams, and business stakeholders.
1+ years of experience deploying agentic AI based applications in enterprise production environments
Job Info
Job Identification 63512
Job Category PD Operations and Quality
Posting Date 08/04/2026, 10:08 PM
Degree Level Bachelor's Degree or equivalent
Job Schedule Full time
Locations 3120 139th Ave SE, Bellevue, WA, 98005, US (Hybrid)
Preferred Degree Graduate or Equivalent Technical