Sr. Principal AI/ML Data Management & Governance Lead

Lucid Motors•Newark, CA

3d•Hybrid

About The Position

Data generated by electric vehicles, manufacturing operations, and enterprise systems is rich and diverse with increasing potential to design, develop, and deploy AI/ML solutions at scale. Lucid’s AI organization is leading the innovation and in-vehicle AI product solutions and is seeking a Lucid’s AI/ML organization is seeking a Sr. Principal AI/ML Data Management & Governance Lead. This position will strategize, execute, and maintain master data management and data readiness for Lucid’s AI operational solutions and product enablement. You will architect and build the automated "control plane" that ensures our Data, AI, and Autonomous Agents operate in a safe, well-governed manner. This role bridges the gap between policy and code.

Requirements

Bachelors Degree in Computer Science, Electrical Engineering or other field, Masters Degree is preferred
8+ years of experience with a completed Masters Degree
12+ years of experience with a completed Bachelors Degree
Strategic Leadership: Ability to define governance roadmaps with C-suite stakeholders and deliver foundational code.
High-Stakes Experience: Proven track record in industries where data failure impacts safety or regulatory compliance (e.g., Pharma, Healthcare, Defense).
Knowledge graph architectures and semantic layers for AI data standardization.
Policy engines (e.g., OPA) and “Policy as Code” frameworks within IaC workflows.
Enterprise-scale data catalogs and automated lineage systems (e.g., OpenLineage).
Familiarity with modern AI stacks: vector search, LLM orchestration, and RAG architectures.
Multi-cloud security fluency with focus on data residency, encryption, and zero-trust architectures.

Responsibilities

Design Unified Governance Architecture across four critical domains: Core Data Integrity & Lineage, Semantic Governance & Ontologies, AI Explainability & Forensic Safety, and Agentic AI Controls.
Create federated governance models enabling teams to own data products while adhering to global standards.
Architect automated metadata harvesting systems for a central data catalog with rich context tagging.
Implement sovereignty and traceability frameworks to meet strict location-based regulations and enable end-to-end data lineage.
Define and enforce enterprise ontologies and semantic standards as the “source of truth” for AI systems.
Establish governance models for seamless AI agent interoperability.
Build Explainable AI (XAI) frameworks linking decisions to training data versions and metadata.
Design forensic guardrails to reconstruct decision states and prevent PII leakage or hallucinations.
Architect identity frameworks for non-human agents with strict “least privilege” principles.
Define sandboxing standards for agent interactions with external tools to prevent unintended state changes.