Senior AI Data Engineer

General Motors Company; Austin (TX); Híbrido; USD 134.000 - 189.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Incentive pay program

Health, Dental, Vision

GM vehicle discounts

Descripción de la vacante

General Motors GPSC Insights Engineering seeks an experienced data and AI engineer to design and deliver scalable data and AI solutions for purchasing supply chain use cases in a hybrid Austin role. You will own end-to-end delivery, ensuring data is trusted, scalable, secure, and observable for BI, analytics, ML, and generative AI.

The role requires 6+ years in data or software engineering, strong Python/SQL, Spark, Kubernetes, and cloud experience (Azure preferred; AWS/GCP also considered).

Formación

• Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or related field (or equivalent experience).
• 6+ years of experience in data engineering, software engineering, platform engineering, or related discipline, including production delivery of scalable data solutions.
• Proficient in Python and SQL; experience with Scala or another production language is a plus.
• Hands-on experience building data pipelines or platforms supporting ML, analytics, or genAI workloads.

Responsabilidades

• Architect, design, develop, and deliver scalable data and AI engineering solutions across batch, streaming, and near-real-time workloads.
• Assemble, curate, and transform datasets for model training, validation, evaluation, feature generation, embedding generation, retrieval, and inference.
• Design and implement robust source-system integrations across 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 ML and generative AI applications.

Conocimientos

Python

SQL

Apache Spark

Kubernetes

Kafka

Azure

AWS

GCP

LangChain

CrewAI

Vertex AI

LLM evaluation

Educación

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or related field

Master’s degree in a related field

Herramientas

CI/CD

Docker

Descripción del empleo

Join General Motors’ GPSC Insights Engineering team to design and deliver scalable data and AI engineering solutions for purchasing supply chain use cases. This hybrid role is based in Austin, TX and focuses on owning solutions end to end, from discovery through production, so teams can rely on data that is trusted, scalable, secure, and observable for BI, advanced analytics, machine learning, and generative AI.

Compensation: The expected base compensation for this role is $134,000 - $188,600 per year. An incentive pay program provides payouts based on company performance, job level, and individual performance.

What you will deliver

• Architect, design, develop, and deliver 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 robust source-system integrations across 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.
• Create AI-enabled data and application workflows using appropriate AI 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, documenting tradeoffs and driving decisions for complex problems with limited precedent.

Collaboration and engineering practices

• Partner with data scientists, ML engineers, product owners, business stakeholders, platform teams, cybersecurity, and governance partners to translate 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 risks, progress, and results to technical and non-technical audiences using data to demonstrate value and address concerns.
• Mentor peers and junior engineers through guidance on difficult technical issues, 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.

What you bring

• 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 building data pipelines or platforms supporting machine learning, advanced analytics, or generative AI workloads, including data preparation, feature or embedding generation, evaluation, or inference 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 operating 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 practices.
• Experience with data architecture, modeling, schema design, data contracts, relational and non-relational stores, object storage, and APIs.
• Strong understanding of performance optimization including 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 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, with the ability 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 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.

Hybrid work and eligibility

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

Benefits

• Incentive pay program with payouts based on company performance, job level, and individual performance.
• Medical, Dental, Vision
• Health Savings Account and 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

Accommodations

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