Staff AI Engineer – Analytics & Domain Intelligence

General Motors•Austin, TX

1d•$171,700 - $220,500•Hybrid

About The Position

This role is categorized as hybrid. This means the successful candidate is expected to report to Warren Global Technical Center or Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. We’re looking for a hands-on Staff AI Engineer to build the next generation of analytics experiences on top of trusted telematics, aftersales, and energy data. This role focuses on turning complex data into clear, natural-language answers to important business questions—not generic chatbots or demos, but real systems that help people ask better questions, get trusted answers, and make decisions faster. You will work at the intersection of AI, analytics, software engineering, and data products—transforming governed datasets and domain expertise into scalable, natural-language intelligence. You will help turn reusable data products into AI-ready experiences that support engineers, analysts, product teams, leaders, customer care teams, service operations, and other users across the enterprise.

Requirements

8+ years of experience in software engineering, AI engineering, data platforms, or related fields, including senior or staff-level technical leadership.
Bachelor’s degree in Computer Science, Software Engineering, Data Science, Computer Engineering, Information Systems, or a related technical field, or equivalent practical experience.
Proven experience building production AI applications, agentic systems, or LLM-powered analytical experiences.
Strong Python engineering skills and exceptional SQL skills.
Strong understanding of semantic engineering, analytical data products, business semantics, and domain context, and how they influence AI answer quality and trustworthiness.
Experience exposing governed, curated, or contract-backed data products and semantic models to AI systems.
Experience building AI-powered analytical solutions on top of large-scale distributed data platforms.
Experience building and operating production systems in a major cloud environment (Azure, AWS, or GCP).
Experience designing for performance, scalability, reliability, and cost efficiency.
Strong judgment around analytical correctness, not just response fluency or model quality.
Ability to work effectively with analysts, engineers, data scientists, ML scientists, and domain experts.

Nice To Haves

Experience with Databricks, Unity Catalog, Agent Bricks, LangGraph, LangChain, LangSmith, or similar technologies.
Experience with retrieval-augmented generation, grounding, evaluation, reasoning, and explainability in production environments.
Experience with MLOps, monitoring, feedback loops, and continuous-improvement practices for AI systems.
Experience working with telemetry, IoT, sensor, operational, or other event-rich data environments.
Track record of translating complex data and AI concepts into clear, actionable insights for non-technical stakeholders.
Demonstrated ability to lead cross-functional initiatives and influence without direct authority.
Strong written and verbal communication skills, with a focus on clarity, transparency, and trust.

Responsibilities

Build AI-powered analytics experiences grounded in trusted, governed data products and domain context.
Design and develop multi-agent systems, orchestration patterns, supervisor-agent frameworks, and reusable AI services that turn data into usable intelligence.
Create natural-language experiences that help users ask business questions without needing to know schemas, dashboards, or SQL.
Partner with data product teams to ensure AI capabilities are built on high-quality, curated, and contract-backed datasets.
Build retrieval, grounding, reasoning, memory, and evaluation patterns that improve trust, explainability, and answer quality.
Develop reusable frameworks that allow domain experts to contribute and improve business intelligence without deep AI expertise.
Use platform telemetry, feedback, and usage patterns to improve accuracy, usefulness, and adoption over time.
Optimize production AI systems for performance, reliability, scalability, and cost efficiency.