Senior AI Data Engineer - GPSC Insights Engineering

General Motors•Austin, TX

1d•$134,000 - $188,600•Hybrid

About The Position

Join GM’s GPSC Insights Engineering team, which ideates, incubates, and delivers data solutions for global purchasing supply chain (GPSC) and its partners. As a Senior AI Data Engineer, you will design, build, and optimize industrialized data assets, AI data products, and data platforms for Business Intelligence, advanced analytics, machine learning, and generative AI. You will own solutions from discovery through production, ensuring data used for training, evaluation, retrieval, inference, and decisioning is trusted, scalable, secure, observable, and fit for purpose. Within the GPSC Insights Engineering organization under the Data organization, you will independently lead complex initiatives and drive delivery across teams. You will apply data- and software-engineering expertise to solve novel problems, shape architecture and technology strategy, influence partners with clear recommendations, and deliver significant impact across the function. The role is hybrid and requires reporting to Austin, Texas at least three times per week, or more frequently if business needs dictate. The role may be eligible for relocation benefits. Expected base compensation is $134,000–$188,600; actual compensation varies based on position-related factors. An incentive-pay program offers payouts based on company performance, job level, and individual performance. GM offers benefits including health and wellbeing programs, medical, dental, and vision coverage, Health Savings and Flexible Spending Accounts, retirement savings, sickness and accident benefits, life insurance, paid vacation and holidays, tuition assistance, an employee assistance program, and GM vehicle discounts. GM does not provide immigration-related sponsorship for this role.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related field, or equivalent experience.
At least 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.
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 is preferred, while AWS or GCP are 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 teams and disciplines and drive outcomes without direct authority.

Nice To Haves

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.
Experience with Scala or another production programming language.

Responsibilities

Own 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 structured, semi-structured, and unstructured datasets for model training, validation, evaluation, feature generation, embedding generation, retrieval, and inference.
Design and implement 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 data contracts, schema management, lineage, metadata, quality checks, and observability.
Develop and operationalize feature, embedding, and vector-retrieval pipelines for machine learning and generative AI applications where appropriate.
Build AI-enabled data and application workflows using appropriate frameworks and services, including retrieval, evaluation, and workflow orchestration patterns where appropriate.
Establish repeatable MLOps/LLMOps patterns for data and model versioning, experiments and evaluations, CI/CD, deployment automation, monitoring, drift detection, and retraining triggers.
Design for performance, scalability, reliability, resiliency, security, privacy, cost, and SLA/SLO attainment.
Lead architecture discussions and technology evaluations, identify tradeoffs, propose alternatives, and drive decisions for complex problems with limited precedent.
Implement process improvements, automation, infrastructure redesign, and developer tooling to increase delivery speed and reduce operational risk.
Partner with data scientists, ML engineers, product owners, business stakeholders, platform teams, cybersecurity, and governance partners to turn customer needs into 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 and raise engineering standards through technical guidance, 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.

Benefits

Medical, dental, and vision benefits
Health Savings Account
Flexible Spending Accounts
Retirement savings plan
Sickness and accident benefits
Life insurance
Paid vacation and holidays
Tuition assistance programs
Employee assistance program
GM vehicle discounts
Potential incentive pay based on company performance, job level, and individual performance
Potential relocation benefits