Ofertas › General Motors
General Motors · Austin, TX; Warren, MI
Description The Role The AI Engineering and Productivity team in the Global Planning, Design, and Product IT org is responsible for empowering Engineering and Quality teams by delivering reliable and secure AI-driven tools that streamline workflows, accelerate decision making, surface actionable insights, and unlock measurable productivity gains across the product lifecycle. As a Senior Software Engineer , you will be responsible for developing data intensive and AI-enabled software applications that support Product Development engineering teams. You will work with complex data requirements and deliver solutions using industry standard practices across SQL, Databricks, and modern programming languages such as Java and Python. You will design and build software and data solutions based on detailed requirements and system specifications, with a strong focus on data quality, performance, and reliability. This role offers the opportunity to advise, coach, and mentor other software engineers while shaping how engineering teams access and use data and AI. You will help define patterns and best practices for SQL, Databricks (DBX), and AI/ML integrations within VEQ and play a key role in ongoing modernization and cloud initiatives. What You'll Do Design, develop, and maintain data driven and AI-enabled applications and services that support Product Development engineering teams. Write high-quality, performant SQL (queries, functions, stored procedures) for complex data transformations and modeling across enterprise data platforms (e.g., SQL Server, Oracle, PostgreSQL). Build and optimize data pipelines and workflows in Databricks (DBX) and related tools to support batch and near realtime data processing. Develop backend services and APIs in Java and/or Python that integrate data, business rules, and user workflows into robust, reusable components. Develop enterprise grade applications using Kubernetes/Docker, Quarkus, Java, Angular, PostgreSQL, and other GM approved tools. Partner with data science and AI teams to productionize AI/ML and LLM based solutions, including feature pipelines, inference integrations, monitoring, and continuous improvement. Proactively identify and remediate issues related to code quality, patterns, performance, security, and data correctness, using code quality analysis tools and remediation techniques. Lead or contribute to solution design, including architecture, patterns, and technology choices aligned with GM standards and Statement