AI Data Foundation Engineer

Ford Motor Company

AI Data Foundation Engineer driving AI-first supply chain transformation at Ford. Engineering data pipelines and AI-ready platforms for high-performance supply chain solutions.

Posted 7/15/2026full-timeChennai • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in Data Engineering and AI/ML practices, with a strong focus on designing and maintaining data pipelines, data modeling, and implementing AI-driven development workflows. Proficient in cloud services and data integration patterns to support scalable and high-quality data solutions.

Highest-signal resume keywords

Python ProficiencySQL ProficiencyData Pipeline OrchestrationCloud Services Expertise (GCP/BigQuery/Dataflow)Data Modeling (Relational, Dimensional, Graph)

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data EngineeringAI/ML SolutionsGraph Query Languages (Cypher, Gremlin)Distributed Data Processing Frameworks (Spark, Beam, Dataflow)Data Warehousing ConceptsData Validation and VersioningData Contracts and SchemasAI-Specific SDLC ImplementationData Integration PatternsAutomated Pipeline Maintenance

Soft Skills

Analytical SkillsProblem-Solving SkillsCritical ThinkingCommunication SkillsInterpersonal Skills

Tools & Technologies

AirflowDagsterCloud ComposerGitHub CopilotGenerative AI Tools

Industry Keywords

Supply ChainDecision IntelligenceKnowledge GraphData Foundation EngineeringN-Tier Supplier Network

Tech Stack

Tools & technologies

AirflowBigQueryCloudERPGoogle Cloud PlatformPythonSDLCSparkSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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.