AI Engineer

Ford's Industrial Systems Data Platform (ISDP) unifies data from Product Development, Manufacturing, Quality, and Supply Chain applications into a single logical data model. We are building an AI-queryable knowledge graph over that data , a BigQuery property graph derived from a canonical domain event store , and an MCP (Model Context Protocol) server that lets agents reason over it.

We are looking for an AI Engineer to own this system end to end: design, develop, test, and deploy the graph layer to Production on a Google Cloud Platform–native stack, and build the agent-facing serving layer on top of it. You will turn raw industrial event data into a governed, traversable graph that powers Ford's next generation of engineering, manufacturing, quality, and supply-chain agents.

Responsibilities

Graph Layer

• Design, develop, test, and deploy the ISDP knowledge graph from the domain event store to Production using cloud-native data pipelines.
• Model and evolve the graph's entities and relationships as new data sources are onboarded.

MCP serving layer

• Design, build, and operate the MCP server that exposes the ISDP graph layer and event store as tools to consumers , including GQL graph query, event-store query, and schema/DDL discovery.
• Define tool contracts, context, and guardrails so agents produce grounded, accurate, non-hallucinated responses over the graph.
• Ensure low-latency, secure, and cost-efficient serving for interactive and batch agent workloads.

Reliability, monitoring & observability

• Own monitoring and observability of the graph layer and the MCP server , data freshness, pipeline health, query latency/cost, tool-call success rates, and answer quality.
• Instrument SLOs, dashboards, alerting, and tracing; drive incident response and continuous reliability improvements.

Collaboration & data onboarding

• Partner with Data Engineers and Application Data Source Owners across Product Development, Manufacturing, Quality, and Supply Chain to ingest and validate their data into ISDP.
• Establish data contracts, schema validation, and quality checks; support source owners through onboarding, mapping to the ISDP logical model, and troubleshooting.
• Contribute to data governance, cataloging, and lineage for the graph and its sources.

Qualifications

Required

• 5+ years of experience with Strong software engineering in Java, Python, with production-grade testing, CI/CD, and code quality practices.
• 3+ years of experience with Hands-on experience deploying data/AI systems to Production on a GCP-native stack: Vertex AI, BigQuery, Dataflow / Apache Beam, Pub/Sub, Cloud Run / GKE, Cloud Storage, and Cloud Build / Artifact Registry.
• 3+ years of experience with graph data modeling and querying , property graphs and GQL / graph query patterns (BigQuery property graphs, or equivalent such as Neo4j/Spanner Graph).
• 3+ years of Hands-on experience with Vertex AI (Agents, model serving, embeddings) and evaluation of agent answer quality.
• 2+ years of Experience building LLM/agent systems: tool-use, RAG/grounding, and integrating models via APIs (e.g., Vertex AI or enterprise LLM gateways). Familiarity with MCP or comparable agent tool protocols.
• Observability expertise: Cloud Monitoring/Logging, OpenTelemetry, SLOs, dashboards, and alerting for data pipelines and services.
• 3+ years of experience with Infrastructure as Code (Terraform) and secure-by-default engineering (IAM, least privilege, secrets management).
• Ability to work directly with data producers to model and validate real-world industrial/enterprise data.

Preferred

• Familiarity with Dataplex / Data Catalog for governance, lineage, and business glossaries.
• Streaming/CDC and event-driven architectures; append-only/event-sourced data modeling.
• Design and build user-facing applications and dashboards that surface Knowledge Graph data to end users.
• Domain exposure to PLM / product development, manufacturing execution, quality, or supply-chain systems and their data.
• Data quality frameworks, schema evolution, and blue-green/zero-downtime data deployments.

Benefits

• Immediate medical, dental, vision, and prescription drug coverage
• 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
• Generous vacation time, sick/personal time, and paid holidays, including between Christmas Eve and New Year's Day