Software Engineering Manager – AI Development

General Motors•Austin, MI

20h•Hybrid

About The Position

The Software Engineering Manager– AI Development will lead a high-performing engineering team delivering AI-powered products and platforms that enable data-driven decision making across the enterprise. This role combines deep technical leadership with hands-on people management. The role has a strong focus on building solutions on GM’s Glean and DBX Genie platforms, modern data platforms, and enterprise-grade application architectures. This leader will shape technical strategy, own delivery outcomes end to end, and partner across product, data, business, architecture, infrastructure, and security teams to deliver scalable, secure, and business-ready AI solutions. The role is expected to help drive GM’s broader AI direction while mentoring engineering talent and establishing a strong culture of technical excellence, operational discipline, and innovation.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
10+ years of software engineering experience, including substantial experience as a senior/principal engineer or technical lead.
5+ years of direct people-management experience leading software engineering or AI/ML engineering teams
Proven experience building and deploying AI-enabled solutions; including prompt/workflow design, orchestration, integration, and evaluation.
Strong data platform experience, including modern data lakes/lakehouses/warehouses, data products, APIs, pipelines, governance, security, and data quality concepts.
Full-stack engineering expertise, including modern backend frameworks, frontend frameworks, and API design/integration.
Hands-on cloud experience, preferably Azure, including containerization, microservices, and CI/CD pipelines.
Demonstrated ability to lead complex, cross-team initiatives while balancing technical depth, execution discipline, and stakeholder management.
Strong communication, collaboration, and organizational leadership skills.

Nice To Haves

Experience integrating LLMs and generative AI capabilities into production systems, including safety, observability, and AI behavior evaluation.
Experience collaborating with data science and MLOps teams to operationalize models in production, including monitoring, retraining, and versioning.
Familiarity with enterprise platforms and standards in large, regulated organizations, including identity/access management, compliance, and risk controls.
Demonstrated success in enterprise-scale AI deployments and embedding AI into mission-critical workflows.
Experience leading cloud migrations and modernization efforts for AI and data platforms.
Ability to influence architecture decisions, drive innovation, and lead organizational change across technical and business boundaries.

Responsibilities

Lead and grow an engineering team of software and AI engineers, including hiring, coaching, performance management, career development, and day-to-day delivery leadership.
Own end-to-end delivery of AI-enabled applications and services, from problem definition and architecture through implementation, testing, deployment, and ongoing support.
Lead the design and implementation of AI solutions on Glean and DBX platforms, Agents and DBX Genie, translating business use cases into scalable, reliable, and governed solutions.
Shape technical strategy and architecture for AI and data-driven products, including platform patterns, integration strategy, security, observability, resilience, and operational support.
Drive best practices in software engineering across frontend, backend, data, and workflow layers, including clean architecture, automated testing, CI/CD, and secure coding.
Partner with data platform teams to design and consume curated data products, APIs, and pipelines that support AI and analytics workloads at scale.
Establish and enforce engineering standards for code quality, documentation, design reviews, reliability, and production excellence.
Collaborate cross-functionally with product management, UX, enterprise architecture, infrastructure, governance, and security teams to align roadmaps and remove delivery blockers.
Mentor senior and principal engineers, building technical depth and leadership capacity across the team.
Identify emerging AI capabilities and practical opportunities to integrate them into GM workflows and platforms in a safe, scalable way.