AI Data Engineering Manager - GPSC Insights Engineering

General Motors • Austin, TX

5d • Hybrid

About The Position

Join General Motors’ GPSC Insights Engineering team, which ideates, incubates, and delivers data solutions for global purchasing supply chain (GPSC), Supplier Quality (SQ), and partners. As AI Data Engineering Manager in the GPSC Insights Engineering organization under the Data organization, you will lead a multidisciplinary team that builds and operates industrialized data assets, AI data products, and data platforms supporting business intelligence, advanced analytics, machine learning, and generative AI use cases.

You will own an AI data engineering area, translate functional strategy into a team direction and roadmap, and ensure delivery of trusted, scalable, secure, observable, fit-for-purpose data products. You will lead individual contributors and, as appropriate, technical leaders across complex initiatives spanning teams and departments. The role combines technical judgment with people leadership, workforce planning, resource allocation, stakeholder influence, operational accountability, and change leadership to deliver outcomes aligned with GM and GPSC priorities.

The role is hybrid, with the selected candidate expected to report to a specific location at least three times a week or at another frequency set by their manager. Relocation benefits are available to candidates who qualify under company policy. The job posting states that GM does not provide immigration-related sponsorship for this role. After a successful motor vehicle report review, the employee may be eligible for the company vehicle evaluation program; participants are required to purchase or lease a qualifying GM vehicle every four years unless an exception applies.

Requirements

• 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 is preferred, and AWS or GCP are 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 and 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.

Nice To Haves

• 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.

Responsibilities

• Lead and develop a diverse team of data engineers, AI/ML platform engineers, software engineers, and technical leads; establish roles, objectives, priorities, and success measures.
• Translate GPSC and Data organization strategies into an actionable AI data engineering roadmap, operating plan, delivery commitments, and team objectives and KPIs.
• Own the architecture, delivery, and operational health of scalable data and AI engineering capabilities across batch, streaming, and near-real-time workloads.
• Guide the assembly, curation, and transformation of structured, semi-structured, unstructured, and multimodal datasets for model training, validation, evaluation, feature and 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, ensuring appropriate enterprise patterns are applied.
• Establish policies, processes, and reusable frameworks for integrating source systems, including 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 affecting 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 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.
• Oversee production support, incident response, root-cause analysis, and preventative improvements for data and AI platforms; ensure commitments are met and address delivery gaps.
• Establish mechanisms to understand customer and partner needs, use data and insights to prioritize work, and raise risks or issues that could 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 team capability through hiring, staffing, career discussions, coaching, succession planning, performance management, recognition, and targeted technical and leadership development.
• Create an inclusive, accountable, and psychologically safe environment where employees share ideas, respectfully challenge assumptions, 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.

Benefits

• Relocation benefits are available for candidates who qualify under company policy.
• Eligible employees may participate in the company vehicle evaluation program, subject to successful motor vehicle report review and program requirements.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager