AI Data Engineering Manager - GPSC Insights Engineering

GM • Austin, TX

3d • Hybrid

About The Position

The AI Data Engineering Manager leads a multidisciplinary team in GM’s GPSC Insights Engineering organization, building and operating industrialized data assets, AI data products, and platforms for business intelligence, advanced analytics, machine learning, and generative AI. The role owns an AI data engineering area, translates functional strategy into a team roadmap, and ensures delivery of trusted, scalable, secure, observable, fit-for-purpose data products.

The manager leads individual contributors and, as appropriate, technical leaders across complex initiatives involving multiple teams and departments. The position combines technical direction with people leadership, workforce planning, resource allocation, stakeholder influence, operational accountability, and change leadership to deliver outcomes aligned with GM and GPSC priorities.

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 direct technical team leadership, including coaching, performance, staffing, prioritization, and delivery outcomes.
• Advanced understanding of data and AI platform architecture, including pipelines, data products, feature or embedding pipelines, model data delivery, and production operations.
• Strong 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 across multiple teams or departments.
• Experience leading teams delivering AI applications or data products with 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, including adapting recommendations for technical, business, and executive audiences.
• Candidates must not require GM immigration sponsorship now or in the future.

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 data engineers, AI/ML platform engineers, software engineers, and technical leads; establish roles, objectives, priorities, and measures of success.
• Translate GPSC and Data organization strategies into an AI data engineering roadmap, operating plan, delivery commitments, and team objectives and KPIs.
• Own architecture, delivery, and operational health of scalable data and AI engineering capabilities across batch, streaming, and near-real-time workloads.
• Guide preparation and transformation of structured, semi-structured, unstructured, and multimodal datasets for model training, validation, evaluation, feature and embedding generation, retrieval, and inference.
• Set engineering standards for data contracts, schema management, lineage, metadata, data quality, observability, security, privacy, responsible AI, and platform reliability.
• Drive repeatable MLOps/LLMOps practices, including 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 using appropriate enterprise patterns.
• Establish policies, processes, and reusable frameworks for source-system integrations involving files, database extracts, APIs, event streams, external suppliers, and supply-chain systems.
• Plan and allocate people, capacity, technology, and budget across the portfolio; reprioritize and reallocate resources as business needs, risks, and opportunities change.
• Lead strategy and implementation of large, cross-department projects, balancing performance, scalability, reliability, resiliency, security, privacy, cost, and SLA/SLO attainment.
• Make and communicate technical decisions; resolve competing demands, sensitive situations, conflicts, and tradeoffs with organizational and partner teams.
• Build relationships with senior business and technology partners, data scientists, ML engineers, product owners, platform teams, cybersecurity, governance, suppliers, and vendors; gain alignment through persuasion and negotiation.
• Oversee production support, incident response, root-cause analysis, and preventative improvements for data and AI platforms; ensure commitments are met and gaps are addressed.
• 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 with emerging AI, data, and engineering technologies while protecting enterprise standards and operational stability.
• Develop team capability through hiring, staffing, career discussions, coaching, succession planning, performance management, recognition, and targeted skills development.
• Create an inclusive, accountable, psychologically safe environment where employees share ideas, respectfully challenge assumptions, learn from setbacks, and own outcomes.
• Model GM behaviors by candidly communicating risks, protecting sensitive information, upholding ethical standards, acting with urgency, collaborating, and considering enterprise impact.

Benefits

• Company vehicle evaluation program: eligible participants are assigned a GM vehicle to drive and evaluate after successful motor vehicle report review; participants must purchase or lease a qualifying GM vehicle every four years unless an exception applies.
• Relocation benefits are available for candidates who qualify under company policy.
• Benefits are described as supporting employee well-being at work and at home; specific benefits are not listed.

What This Job Offers

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