AI Data Foundation Engineer

• 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