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General Motors LLC

AI Data Engineering Manager - GPSC Insights Engineering

Austin, TX, United States
Warren, MI, United States

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Job details

Experience: 3+ years of experience directly leading technical teams, including responsibility for coaching, performance, staffing, prioritization, and delivery outcomes.
Posting language: English
Posted: Nov 3, 2026

Original job description

Job Description

The Role Join our GPSC Insights Engineering team, responsible for ideating, incubating, and delivering new data solutions for global purchasing supply chain (GPSC), Supplier Quality (SQ) and our partners.

As the AI Data Engineering Manager, you will lead a multidisciplinary team responsible for building and operating industrialized data assets, AI data products, and data platforms that support Business Intelligence, Advanced Analytics, machine learning, and generative AI use cases. You will own a distinct AI data engineering area, translate functional strategy into a clear team direction and roadmap, and ensure delivery of trusted, scalable, secure, observable, and fit-for-purpose data products.

In this role within the GPSC Insights Engineering organization under the Data organization, you will lead individual contributors and, as appropriate, other technical leaders across complex initiatives spanning multiple teams and departments. You will combine advanced technical judgment with people leadership, workforce planning, resource allocation, stakeholder influence, operational accountability, and change leadership to deliver measurable outcomes aligned with GM and GPSC priorities.

What You'll Do

Lead and develop a diverse team of data engineers, AI/ML platform engineers, software engineers, and technical leads; establish clear roles, objectives, priorities, and measures of success.
Translate GPSC and Data organization strategies into an actionable AI data engineering roadmap, operating plan, delivery commitments, and team-level objectives and key performance indicators.
Own the architecture, delivery, and operational health of scalable data and AI engineering capabilities across batch, streaming, and near-real-time workloads.
Guide the team in assembling, curating, and transforming structured, semi-structured, unstructured, and multimodal datasets for model training, validation, evaluation, feature generation, embedding generation, retrieval, and inference.
Set technical direction and engineering standards for data contracts, schema management, lineage, metadata, data quality, observability, security, privacy, responsible AI, and platform reliability.
Drive adoption of repeatable MLOps/LLMOps patterns, including data and model versioning, experiment and evaluation workflows, CI/CD, deployment automation, monitoring, drift detection, and retraining triggers.
Set technical direction for AI application development, retrieval, evaluation, and orchestration capabilities, ensuring the team applies appropriate enterprise patterns.
Establish policies, processes, and reusable frameworks for source-system integrations from files, database extracts, APIs, event streams, external suppliers, and supply-chain systems.
Plan and allocate people, capacity, technology, and budget across the team’s portfolio; reprioritize and reallocate resources as business needs, risks, and opportunities change.
Drive strategy and implementation of large projects that impact multiple departments, balancing performance, scalability, reliability, resiliency, security, privacy, cost, and SLA/SLO attainment.
Make and communicate key technical decisions within the managed area; resolve competing demands, sensitive situations, conflicts, and tradeoffs within the organization and with partner teams.
Build strong relationships with senior business and technology partners, data scientists, ML engineers, product owners, platform teams, cybersecurity, governance, suppliers, and vendors; use persuasion and negotiation to gain alignment on new approaches.
Oversee production support, incident response, root-cause analysis, and preventative improvements for data and AI platforms; ensure the team meets commitments and addresses gaps when results fall short.
Establish mechanisms to understand customer and partner needs, use data and insights to prioritize work, and raise risks or issues that could negatively affect business outcomes or customer experience.
Lead change management and bounded experimentation to evaluate emerging AI, data, and engineering technologies while protecting enterprise standards and operational stability.
Build the team’s capability through hiring, staffing, career discussions, coaching, succession planning, performance management, recognition, and targeted development of technical and leadership skills.
Create an inclusive, accountable, and psychologically safe environment where employees share ideas, challenge assumptions respectfully, learn from setbacks, and own outcomes.
Model GM behaviors by speaking candidly about risks, protecting sensitive information, holding partners to ethical standards, moving with urgency, collaborating as one team, and considering enterprise impact in decisions.

Your Skills & Abilities (Required Qualifications)

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Data Science, or a related field, or equivalent experience.
10+ years of experience in data engineering, software engineering, platform engineering, AI/ML engineering, or a related discipline, including production delivery of scalable data solutions.
3+ years of experience directly leading technical teams, including responsibility for coaching, performance, staffing, prioritization, and delivery outcomes.
Advanced understanding of data and AI platform architecture, including data pipelines, data products, feature or embedding pipelines, model data delivery, and production operations.
Strong technical proficiency in Python and SQL, with working knowledge of distributed data processing such as Apache Spark and container orchestration such as Kubernetes.
Experience with streaming and event-driven data solutions using Kafka or comparable technologies, including operation in Kubernetes-based environments.
Experience with cloud platforms; Azure preferred, with AWS or GCP also considered.
Demonstrated experience establishing engineering standards for CI/CD, infrastructure automation, testing, observability, incident response, security, and production support.
Experience with data architecture, modeling, schema design, data contracts, relational and non-relational data stores, object storage, APIs, and data governance.
Working knowledge of machine learning and generative AI data lifecycle requirements, including training/evaluation data, feature engineering, embeddings, retrieval, inference, monitoring, and data/model drift.
Experience setting objectives, managing roadmaps, allocating resources, tracking KPIs, communicating status and risks, and delivering results across multiple teams or departments.
Experience leading teams that deliver AI applications or data products using AI development frameworks and services such as LangChain, CrewAI, Vertex AI, or comparable technologies.
Working knowledge of RAG/vector search, FastAPI/React application patterns, LLM evaluation, and agent orchestration.
Strong communication, influencing, conflict-resolution, and negotiation skills, with the ability to adapt recommendations for technical, business, and executive audiences.

What Can Give You a Competitive Advantage (Preferred Qualifications)

Master’s degree in Computer Science, Software Engineering, Data Science, Business, or a related field.
Experience managing managers, technical leads, or teams with diverse engineering and data/AI specialties.
Experience with generative AI or LLM data platforms, retrieval-augmented generation, prompt and evaluation datasets, vector databases, embeddings, or knowledge retrieval systems.
Experience with feature stores, model registries, experiment tracking, model serving, data/model monitoring, or other MLOps/LLMOps capabilities.
Experience with Databricks, Snowflake, Azure Machine Learning, MLflow, or similar platforms.
Experience designing ingestion and processing frameworks for heterogeneous and multimodal data such as documents, video, audio, and images.
Knowledge of data observability, model/data drift, bias or fairness considerations, explainability, and controls for trustworthy AI.
Experience with workforce planning, operating budgets, vendor or supplier management, financial stewardship, and large-scale technology change.
Experience with automotive, manufacturing, global purchasing, supplier, or supply-chain data domains.

Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.

GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc).

This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.

Relocation benefits are available for candidates who qualify under company policy.

About GM Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Benefits Overview From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.

Non-Discrimination and Equal Employment Opportunities (U.S.) General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.

We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

Accommodations General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

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More about this job

Skills

Data engineering
Procurement
Supply chain management
Business intelligence
Machine learning
Leadership
ML
CI/CD
Cybersecurity
Negotiation
Control production
Incident response
Root cause analysis
Customer experience
Change management
Coaching
Performance management
Computer science
Prioritization
Python
SQL
Process data
Apache Spark
Kubernetes
Apache Kafka
Microsoft Azure
AWS
Google Cloud
React
Communication skills
Conflict resolution
Databricks
Snowflake
Mlflow
Vendor management
CPT
J

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