AI Systems Engineering Manager – HRIS

General Motors

HRIS Systems Engineering Manager leading and supporting engineering for HR technology platforms at General Motors. Focus on modernization, security, and team development in hybrid work setting.

Posted 6/10/2026full-timeAustin • California, Missouri, Texas • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Lead, coach, and develop a team of engineers, providing technical guidance, career development support, and ongoing performance feedback.
Set the engineering vision, roadmap, and execution strategy for HRIS platforms in alignment with organizational goals and enterprise architecture standards.
Modernize legacy platforms through automation, cloud enablement, improved engineering practices, and scalable platform design.
Guide the full engineering lifecycle including requirements definition, architecture, implementation, release, support, and continuous improvement.
Drive platform engineering practices including infrastructure as code, CI/CD, observability, resiliency, and secure-by-design delivery.
Partner with HR, security, infrastructure, architecture, delivery, data, and enterprise platform teams to align technical solutions with business needs.
Build team capability in automation, scripting, cloud deployment, modern platform tools, and engineering best practices.
Implement and continuously improve performance management processes for the team, including goal setting, regular reviews, development planning, and accountability for results.
Develop and manage plans, priorities, resource allocation, and execution risks to ensure initiatives are delivered on time and with high quality.
Identify, assess, and mitigate technical and operational risks while driving pragmatic solutions to complex engineering challenges.
Ensure platforms are secure, stable, scalable, well governed, and compliant with company standards and security requirements.
Communicate effectively with technical and non-technical stakeholders, translating complex concepts into clear and actionable decisions.

Requirements

What you’ll need

Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related field, or equivalent practical experience.
Experience leading technical teams in systems engineering, platform engineering, enterprise applications, infrastructure engineering, or HR technology environments.
Experience setting technical direction, driving delivery, and developing engineering talent.
Strong experience designing and implementing solutions for complex enterprise platforms.
Experience leading modernization efforts involving automation, cloud enablement, and legacy transformation.
Experience with infrastructure as code, cloud deployment, scripting, and CI/CD tools.
Strong knowledge of security practices including RBAC, secrets management, patching, vulnerability remediation, SSO, and SAML.
Strong communication, problem-solving, analytical, and cross-functional collaboration skills.
Proven ability to lead through ambiguity, influence stakeholders, and align teams around a common technical direction.
Working knowledge of Artificial Intelligence (AI) and Machine Learning (ML) concepts, including supervised and unsupervised learning, model lifecycle, and data pipelines.
Experience integrating AI-driven capabilities into enterprise platforms, such as intelligent automation, predictive insights, conversational interfaces, decision-support tools, or workflow augmentation.
Familiarity with AI-enabled platforms and tools, including cloud-based AI services, large language models, intelligent process automation, analytics platforms, or similar emerging technologies.
Ability to partner with data science, analytics, and platform teams to translate business needs into AI-enabled platform requirements, architectures, and practical use cases.
Understanding of data pipelines, data governance, and data quality practices required to support responsible and scalable AI solutions.
Understanding of model risk management and ethical AI considerations, including bias, transparency, explainability, privacy, and security.
Experience applying AI to improve engineering productivity, platform operations, monitoring, testing, support workflows, or service reliability.
Ability to evaluate AI opportunities pragmatically and drive adoption in ways that improve outcomes, reduce manual effort, and strengthen platform capability.
Experience with HRIT, HRIS, Workday, or other enterprise business systems.
Experience leading platform modernization, cloud migration, and automation adoption.
Familiarity with DataDog, OpenTelemetry (OTEL), GitHub, or similar engineering tools.
Experience with enterprise integration patterns, APIs, and modern platform architectures.
Experience leading teams through technology modernization and AI adoption initiatives.

Benefits

Comp & perks

From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions.
Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.

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Hard Skills & Tools

infrastructure as codecloud deploymentscriptingCI/CDautomationsecurity practicesAI conceptsMachine Learning conceptsdata pipelinesenterprise platform design

Soft Skills

communicationproblem-solvinganalytical skillscross-functional collaborationleadershipinfluenceambiguity managementteam alignmentperformance managementtechnical guidance