[Hiring] Software and AI Engineer @Ford

Role Description

Ford Motor Credit Company's Pinnacle Loan Originations platform underwrites and funds a significant share of the vehicles Ford sells worldwide. It is a mission-critical, multi-tier system:

• A mainframe core (COBOL, DB2, CICS) that executes the money-path decisioning
• A Java/Spring services tier that orchestrates business workflows
• GCP Cloud Run microservices at the digital edge
• Splunk-driven observability across the estate

We are building an AI enablement layer on top of this platform — a set of production capabilities that make Pinnacle faster to operate, easier to debug, and more discoverable to the engineers and analysts who depend on it.

What you'll do:

• Read the whole stack, then change it. Diagnose production issues across the mainframe (COBOL, DB2, CICS), the Java/Spring services tier, and Cloud Run microservices.
• Ship the fix on the money-path and the corresponding improvement to agent tooling and knowledge substrate in the same change set.
• Design and operate the MCP tools layer. Build least-privilege, per-call-audited MCP servers over DB2, Splunk, and GCP. Provenance, PII handling.
• Build production multi-agent workflows on Google ADK — SequentialAgent and LoopAgent compositions, creating and managing Agent workflows, including escalation-containment patterns — for Splunk → DB2 → GCP error triage, ADR authoring, and originations debugging.
• Own the knowledge graph and retrieval work end-to-end. Model the loan originations ontology (VIN, dealer, offer, program eligibility, environment topology). Implement hybrid vector + graph retrieval. Every RAG-produced answer must be defensible under audit.
• Ship deterministic backend services in Java/Kotlin + Spring Boot on GCP — the audit envelope, provenance store, and human-in-the-loop review surfaces where non-determinism is unacceptable.
• Author ADRs in ADR-driven-development, OKF business-logic documents, skill files, and Splunk saved-search catalogs. Pair with teammates on the hard debugging sessions. The write-up is part of "done."
• Track mean time from "agent cannot handle this" to "agent handles this autonomously," and publish the trend monthly.
• Create evaluations and an evaluation runner for all AI tools and agents built by you and the team.
• Publish, monthly, the change types you've made cheaper — mean time from "agent doesn't know this" to "agent handles it autonomously."
• Automate test-case generation, evaluation, and grading for both brownfield and greenfield applications, ensuring coverage, repeatability, and measurable quality improvements.

Qualifications

• Bachelor's or Master’s degree in Computer Science, Information Systems, or a related field — or equivalent professional experience.
• 7+ years building and operating production-grade backend systems as a hands-on engineer.
• 5+ years in Python and Java (Kotlin, TypeScript a plus).
• At least 1 year of experience in shipping AI-enabled applications in production — agent frameworks, retrieval systems, or LLM-backed services.
• Demonstrated experience with agent orchestration frameworks — Google ADK, LangGraph, LangChain, or comparable.
• Practical experience implementing MCP (Model Context Protocol) tool integrations, agent communication patterns, and AI observability.
• Working experience with vector search and hybrid retrieval, and with knowledge graphs or GraphRAG.
• Strong backend fundamentals: Spring Boot, relational databases (PostgreSQL, DB2, or similar) including query and schema design, REST/OpenAPI, TDD, modern CI/CD.
• Production experience on Google Cloud Platform (Vertex AI, Cloud Run, BigQuery) or an equivalent cloud.
• Comfort operating in complex, multi-tier, legacy-integrated environments where change control and auditability matter.

Even Better you'll have...

• Semantic technologies, ontology engineering, or graph databases (Neo4j, Neptune, or similar).
• Experience integrating with mainframe systems (COBOL, DB2 for z/OS, CICS) or comparable long-lived transactional cores.
• Splunk SPL proficiency, including saved-search and dashboard authoring on large indexes.
• Event-driven architectures (Pub/Sub, Kafka); containerization (Docker, Kubernetes).

Benefits

• Immediate medical, dental, vision and prescription drug coverage
• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
• Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
• Vehicle discount program for employees and family members and management leases
• Tuition assistance
• Established and active employee resource groups
• Paid time off for individual and team community service
• A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
• Paid time off and the option to purchase additional vacation time.