Senior AI Data Engineer – GPSC Insights Engineering

General Motors

. Design, build, optimize, and productionize scalable data and AI engineering solutions across batch, streaming, and near-real-time workloads .

Posted 11/6/2026full-timeAustin • Texas • United StatesSenior💰 $134,000 - $188,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing scalable data and AI engineering solutions, with a strong focus on data pipeline engineering, MLOps practices, and collaboration across technical teams. Proficient in Python, SQL, and cloud platforms, with a solid understanding of data architecture and machine learning workflows.

Highest-signal resume keywords

Data Pipeline EngineeringMLOps/LLMOps ImplementationPython ProficiencyApache Spark ExperienceCloud Platform Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringMachine LearningSQLPythonScalaData ArchitectureSchema DesignKafkaCI/CDData Quality

Soft Skills

CollaborationCommunicationMentoringInfluencing

Tools & Technologies

Apache SparkKubernetesAzureAWSGCPLangChainCrewAIVertex AIMLflowFastAPI

Industry Keywords

Generative AIBig Data TechnologiesData GovernanceSupply Chain DataData Contracts

Tech Stack

Tools & technologies

ApacheAWSAzureCloudCyber SecurityGoogle Cloud PlatformKafkaKubernetesPythonReactScalaSnowflakeSparkSQL

About the role

Key responsibilities & impact

Design, build, optimize, and productionize 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 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 reusable data products with data contracts, schema management, lineage, metadata, quality checks, and observability
Develop feature, embedding, and vector-retrieval pipelines for machine learning and generative AI applications
Build AI-enabled data and application workflows using AI development frameworks and services
Establish MLOps/LLMOps patterns including versioning, experimentation, evaluation, 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, and drive technical decisions
Implement process improvements, automation, infrastructure redesign, and developer tooling
Partner with data scientists, ML engineers, product owners, business stakeholders, platform teams, cybersecurity, and governance partners
Resolve production issues, lead root-cause analysis, and implement preventative improvements
Communicate recommendations, risks, progress, and results to technical and non-technical audiences
Mentor peers and junior engineers and raise engineering standards through design reviews, documentation, and knowledge sharing
Independently own complex data and AI initiatives from discovery through production and measure outcomes by business and platform impact

Requirements

What you’ll need

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
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 supporting machine learning, advanced analytics, or generative AI workloads
Experience with Apache Spark and Kubernetes
Proficiency with Kafka or comparable streaming and event-driven data technologies, including Kubernetes-based environments
Experience with cloud platforms; Azure preferred, with AWS or GCP also considered
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 data stores, object storage, and APIs
Understanding of 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
Master’s degree and experience with generative AI, MLOps/LLMOps, Databricks, Snowflake, Azure Machine Learning, MLflow, multimodal data, trustworthy AI, big-data technologies, or automotive/manufacturing/supply-chain data are preferred qualifications
Must not require GM immigration sponsorship now or in the future

Benefits

Comp & perks

Incentive pay program with payouts based on company performance, job level, and individual performance
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
Relocation benefits may be available