Senior AI Data Engineer - GPSC Insights Engineering

GM•Austin, TX

1d•Hybrid

About The Position

The Senior AI Data Engineer joins General Motors’ GPSC Insights Engineering team, which develops data solutions for global purchasing and supply chain and its partners. The role designs, builds, and optimizes industrialized data assets, AI data products, and platforms that support business intelligence, advanced analytics, machine learning, and generative AI. The engineer owns solutions from discovery through production, ensuring data 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, the position independently leads complex initiatives across teams. It applies data and software engineering expertise to novel problems, shapes architecture and technology strategy, influences partners through data and recommendations, and delivers solutions with significant functional impact. The role is hybrid, with the successful candidate expected to report to Austin, Texas at least three times per week. It may be eligible for relocation benefits. 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 supporting 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, including 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 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 structured, semi-structured, and unstructured data for model training, validation, evaluation, feature and embedding generation, retrieval, and inference.
Design and implement source-system integrations for 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 AI frameworks and services, including retrieval, evaluation, and workflow orchestration patterns where appropriate.
Establish repeatable MLOps and 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 on complex problems.
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 and 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.
Incentive pay program with payouts based on company performance, job level, and individual performance.
Potential relocation benefits.