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General Motors

Senior AI Data Engineer

General Motors
Austin, TX $134k - $189k/yr Full time Posted 40m ago
Senior Artificial Intelligence Big Data Data Analytics Data Architecture Data Engineer Data Engineering Data Integration Data Pipeline Data Platform Data Processing Gen Ai Engineering Llm Operations Machine Learning Data Engineer Rag Systems SQL
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Job Description

General Motors is hiring a Senior AI Data Engineer for the GPSC Insights Engineering team, building scalable data and AI engineering solutions that support purchasing supply chain use cases. This hybrid role is based in Austin, TX and offers an incentive pay program plus a comprehensive benefits package, along with expected base compensation of $134,000 to $188,600.

Hybrid work: report to Austin, TX three times per week at minimum (or other frequency as dictated by the business if more than 3 days). This role may be eligible for relocation benefits. GM does not provide immigration-related sponsorship for this position.

What you’ll do

  • 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 large structured, semi-structured, and unstructured datasets for 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 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 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 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.
  • Partner across data science, ML engineering, product, platform, cybersecurity, and governance to translate customer needs into technical plans.
  • Support production operations by resolving issues, leading root-cause analysis, and implementing preventative improvements across data and AI platforms.
  • Communicate recommendations, risks, progress, and results to technical and non-technical audiences, using data to demonstrate value.
  • Mentor peers and junior engineers through design reviews, documentation, and knowledge sharing, while raising engineering standards.
  • Model GM behaviors: speak candidly about risks, protect sensitive information, experiment responsibly, move with urgency, collaborate inclusively, and own outcomes.

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 language is a plus.
  • Hands-on experience engineering data pipelines or platforms supporting machine learning, advanced analytics, or generative AI, 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 solutions using Kafka (or comparable technologies) including operation in Kubernetes-based environments.
  • Cloud experience: Azure preferred, with AWS or GCP also considered.
  • Solid understanding of CI/CD, infrastructure automation, testing, observability, incident response, and production support.
  • Experience with data architecture, modeling, schema design, data contracts, relational and non-relational stores, object storage, and APIs.
  • Strong understanding of performance optimization (partitioning, clustering, caching, indexing, 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 patterns, LLM evaluation, and agent orchestration.
  • Strong collaboration, communication, and influencing skills to drive outcomes without direct authority.

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 (documents, video, audio, images).
  • Knowledge of data observability, model/data drift, bias or fairness considerations, explainability, and controls for trustworthy AI.
  • Experience with big-data technologies such as Hadoop, Hive, HBase, ADLS/S3, event queues, key-value datastores, document stores, or comparable systems.
  • Experience with automotive, manufacturing, global purchasing, supplier, or supply-chain data domains.

Benefits

  • Incentive pay program with payouts based on company performance, job level, and individual performance.
  • Medical, dental, and 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
  • Expected base compensation: $134,000 - $188,600 (annual)

Accommodations

General Motors offers opportunities to job seekers including individuals with disabilities. If you need a reasonable accommodation for your job search or application, email or call 1-800-865-7580.

Technologies you may work with

  • Python, SQL, Scala
  • Apache Spark, Kubernetes, Kafka
  • Azure, AWS, GCP
  • LangChain, CrewAI, Vertex AI
  • RAG, FastAPI, React
  • MLOps, LLMOps, CI/CD deployment automation, CI, CD
  • Vector retrieval, vector search, LLM evaluation, agent orchestration
  • Retrieval workflow orchestration, drift detection, retraining triggers

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