This role is categorized as hybrid. This means the successful candidate is expected to report to Austin, TX three times per week, at minimum [or other frequency dictated by the business if more than 3 days].
Responsibilities
Join our GPSC Insights Engineering team, responsible for ideating, incubating, and delivering new data solutions for global purchasing supply chain (GPSC) and our partners.
As a Senior AI Data Engineer, you will design, build, and optimize 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 solutions from discovery through production, ensuring that data used for training, evaluation, retrieval, inference, and decisioning is trusted, scalable, secure, observable, and fit for purpose.
In this role within the GPSC Insights Engineering organization under the Data organization, you will independently lead complex initiatives with multiple moving parts and drive delivery across teams. You will apply deep data engineering and software engineering expertise to solve novel problems, shape architecture and technology strategy, influence partners through data and clear recommendations, and deliver solutions with significant impact across the function.
Own the architecture, design, development, and production delivery of scalable data and AI engineering solutions across batch, streaming, and near-real-time workloads.
Assemble, curate, and transform large structured, semi-structured, and unstructured datasets for model training, validation, evaluation, feature generation, embedding generation, retrieval, and inference.
Design and implement robust source-system integrations from files, database extracts, APIs, event streams, external suppliers, and supply-chain systems.
Build high-quality data pipelines and reusable data products with strong data contracts, schema management, lineage, metadata, quality checks, and observability.
Develop and operationalize feature, embedding, and vector-retrieval pipelines where appropriate for machine learning and generative AI applications.
Build AI-enabled data and application workflows using appropriate AI development frameworks and services, applying retrieval, evaluation, and workflow orchestration patterns where appropriate.
Establish repeatable MLOps/LLMOps engineering patterns, including data and model versioning, experiment and evaluation workflows, CI/CD, deployment automation, monitoring, drift detection, and retraining triggers.
Design for functional and non-functional requirements, including performance, scalability, reliability, resiliency, security, privacy, cost, and SLA/SLO attainment.
Lead architecture discussions and technology evaluations; identify tradeoffs, propose alternative approaches, and drive decisions for complex problems with limited precedent.
Identify and implement process improvements, automation, infrastructure redesign, and developer tooling that increase delivery speed and reduce operational risk.
Partner with data scientists, ML engineers, product owners, business stakeholders, platform teams, cybersecurity, and governance partners to translate customer needs into actionable technical plans.
Work with operations teams to resolve production issues, lead root-cause analysis, and implement preventative improvements across data and AI platforms.
Communicate recommendations, risks, progress, and results to technical and non-technical audiences, using data to demonstrate value and address concerns.
Mentor peers and junior engineers, provide guidance on difficult technical issues, and raise engineering standards through design reviews, documentation, and knowledge sharing.
Model GM behaviors by speaking candidly about risks, protecting sensitive information, experimenting responsibly, moving with urgency, collaborating inclusively, and owning outcomes.
Your Skills & Abilities (Required Qualifications)
Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related field, or equivalent experience.
6+ years of experience in data engineering, software engineering, platform engineering, or a related discipline, including production delivery of scalable data solutions.
Strong proficiency in Python and SQL; experience with Scala or another production programming language is a plus.
Hands-on experience engineering data pipelines or platforms that support machine learning, advanced analytics, or generative AI workloads, including data preparation, feature or embedding generation, evaluation, or inference data delivery.
Experience with distributed data processing such as Apache Spark and container orchestration such as Kubernetes.
Proficiency 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.
Solid understanding of CI/CD, infrastructure automation, testing, observability, incident response, and production support practices.
Experience with data architecture, modeling, schema design, data contracts, relational and non-relational data stores, object storage, and APIs.
Strong understanding of performance optimization techniques such as partitioning, clustering, caching, indexing, and workload tuning.
Working knowledge of data quality, governance, metadata, lineage, privacy, access controls, and responsible use of sensitive data in AI systems.
Experience with AI application 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 collaboration, communication, and influencing skills, with the ability to work across multiple teams and disciplines and drive outcomes without direct authority.
What Can Give You a Competitive Advantage (Preferred Qualifications)
Master’s degree in Computer Science, Software Engineering, Data Science, or a related field.
Experience with generative AI or LLM data pipelines, retrieval-augmented generation, prompt/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 Hadoop, Hive, HBase, ADLS/S3, event queues, key-value datastores, document stores, or comparable big-data technologies.
Experience with automotive, manufacturing, global purchasing, supplier, or supply-chain data domains.
What Success Looks Like
You independently own development and delivery of complex data and AI solutions, with plans reviewed at critical points and outcomes measured by business and platform impact.
You improve the reliability, scalability, quality, and usability of data products that enable AI and analytics across GPSC.
You build trusted relationships with senior partners, make technically sound recommendations, and use data and persuasion to align stakeholders.
You mentor engineers, elevate engineering practices, and help teams adopt innovative solutions safely and effectively.
This job may be eligible for relocation benefits.
Conditions
The expected base compensation for this role is: $134,000 - $188,600. Actual base compensation within the identified range will vary based on factors relevant to the position.
Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits : GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
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., H-1B, OPT, STEM OPT, CPT, TN, J-1, etc.)
LI-CC1
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.
Benefits
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 the company team.
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.)
The company 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.
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 .