AI Data Foundation Engineer

As an

AI Data Foundation Engineer in the Supply Chain AI and Decision Intelligence space, you will be the driving force for delivering Ford's "AI-First" supply chain transformation. This role is focused on

engineering the intelligent data backbone , the pipelines, knowledge graphs, and AI-ready data platforms that

fuel high-performance models (internal or external, e.g., from COTS products) and continuously enrich Enterprise Knowledge Graphs to solve complex supply chain problems. Central to this role is the adoption of an AI-Native Data Engineering SDLC, leveraging agentic workflows, AI-assisted coding, and automated testing to deliver robust, high-quality, model-ready data at industrial speed. You will bridge the gap between raw, fragmented enterprise data and the trusted, governed, AI-ready data foundation that powers supply chain AI/ML systems, decision-intelligence engines, and Generative AI applications. This role demands a blend of technical expertise in data engineering, AI/ML data enablement, and platform architecture, functional expertise in supply chain, and strong technical leadership to influence stakeholders and deliver transformative results with a particular focus on establishing and standardizing the AI-centric data engineering SDLC and data quality frameworks across cross-functional teams. You aren't just moving data ,you are engineering the intelligence layer beneath a global supply chain, building the foundation of trust and reliability that allows AI to manage risk, build resilience, and keep factories running.

Responsibilities

• Data Requirement Gathering: Partner with supply chain functional leads , Internal Data Platform teams and AI/ML engineers to elicit and document data requirements and translate them into scalable pipeline and schema designs, ensuring every dataset delivers measurable business value.
• Pipeline & Platform Engineering: Act as the primary technical lead for data foundation engineering. design, build, and maintain ingestion, transformation, and storage pipelines that reliably deliver clean, structured, and timely data to downstream AI/ML consumers within the supply chain GCP space.
• Graph-Based Data Modeling: Work closely with Knowledge Graph engineering and AI teams to design, construct, and maintain ontologies and graph schemas against enterprise data sources, enabling decision-intelligence frameworks that proactively identify and mitigate risks across the global N-tier supplier network. Build and maintain the data pipelines that keep these graphs continuously updated with data from ERP, logistics, and supplier systems — the foundation for "what-if" scenario simulation using Generative AI and Graph analytics.
• AI-Driven SDLC Execution: Champion and implement AI-assisted development practices. Implement agentic workflows (e.g., AutoGen, CrewAI) and Use LLM-based tools (e.g., GitHub Copilot, automated PR agents, and AI-generated documentation) to accelerate delivery with high code quality for the Decision Intelligence platform
• Pipeline & DataOps Engineering: Design the "connective tissue" between source systems, Knowledge Graph updates, and model inference engines. Establish rigorous data validation, versioning, and observability frameworks. Maintain automated pipelines that ensure decision-support tools are always powered by the most current, high-quality data.
• Technical Standardization: Develop reusable data contracts, schemas, and ingestion patterns to ensure that data infrastructure can be scaled across multiple business units without redundant engineering effort.

Preferred Qualifications

• AI-SDLC Experience: Proven track record of using AI tools to enhance personal or team productivity (e.g., Agentic workflows, RAG-based requirement synthesis).
• Data Governance & Cataloging: Experience with data catalog tools (e.g., Collibra, Alation, Dataplex) and metadata management practices.
• Knowledge Graph: Understanding semantic ontologies and how they enable advanced analytics.
• COTS Integration: Experience integrating COTS AI solutions into an enterprise tech stack.
• Supply Chain Domain Knowledge: Functional understanding of supply chain operations, including demand & capacity planning, logistics, sustainability & risk management, resilience, etc.
• Streaming & Real-Time Data: Experience with streaming data platforms (Kafka, Pub/Sub) for near-real-time supply chain event processing.
• Research to Production: Ability to research and rapidly apply & build a functional prototype that meets Ford’s standards for security and scalability.
• Minimum Requirements
• Bachelor’s degree in Computer Science, Data Science, or a related technical field.
• 3+ years of progressive experience in AI/ML, Data Engineering or Data Science, with a proven track record of delivering production-grade solutions in large enterprise environments.
• Strong proficiency in Python and SQL. Deep experience with distributed data processing frameworks (e.g., Spark, Beam, Dataflow) and Graph Query Languages (e.g., Cypher, Gremlin).
• Demonstrated experience with data pipeline orchestration tools (e.g., Airflow, Dagster, Cloud Composer) and CI/CD for data pipelines, and designing/implementing AI-specific SDLCs.
• Strong understanding of data modeling (relational, dimensional, and graph), data warehousing concepts, and building data foundations that support LLM/RAG applications - including chunking strategies, embedding pipelines, and vector store integration.
• Strong technical expertise in cloud services (GCP/BigQuery/Dataflow/Cloud Storage) and data integration patterns.
• Strong analytical, problem-solving, and critical thinking skills.
• Exceptional communication & interpersonal skills, to translate complex AI logic into strategic recommendations for supply chain business leaders.