AI Agent Engineer

General Motors

. Design, build, and scale enterprise integrations and data pipelines across General Motors .

Posted 9/17/2026full-timeAustin • California • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing enterprise integrations, data pipelines, and API-driven solutions while ensuring compliance with security and operational standards. Proficient in leveraging large language models and modern application architectures to enhance integration workflows and performance.

Highest-signal resume keywords

Integration EngineeringREST API DevelopmentPython ProgrammingEnterprise Application IntegrationCloud-Based Integration Patterns

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Integration EngineeringREST API DevelopmentPython ProgrammingData TransformationMiddleware SolutionsEvent-Driven ServicesSchema ManagementError HandlingMonitoringAutomation Frameworks

Soft Skills

Systems ThinkingCommunication SkillsLeadershipProblem SolvingCollaboration

Tools & Technologies

SaaS PlatformsEnterprise AI ToolsGit-Based Version ControlWorkflow OrchestrationLarge Language Models

Industry Keywords

Data GovernanceSecurity PoliciesIdentity IntegrationComplianceOperational Support

Tech Stack

Tools & technologies

CloudETLPython

About the role

Key responsibilities & impact

Design, build, and scale enterprise integrations and data pipelines across General Motors
Connect Serval integrations, SaaS platforms, enterprise AI tools, and GM core business systems through secure, reusable, scalable patterns
Build reusable services, accelerators, and reference integration templates
Align integration designs with data standards, identity guidelines, security policies, and operational requirements
Partner with Security, Privacy, Identity, ILM, and Retention teams on sensitive or regulated-data workflows
Evaluate integration requests and recommend approved platform, configuration, or custom-engineering approaches
Define integration architectures covering system boundaries, data ownership, reliability, observability, failure handling, and sustainment
Design, build, and maintain integrations using APIs, webhooks, scripts, connectors, middleware, and orchestration logic
Define data mappings, transformation logic, exception handling, validation rules, and monitoring approaches
Modernize legacy interfaces and workflows into scalable API-driven and event-based patterns
Connect Serval integrations with identity services, reporting environments, asset repositories, enterprise applications, and approved AI platforms
Support end-to-end workflows that move data and trigger actions across enterprise systems
Troubleshoot multi-system integration failures and improve performance, reliability, and supportability
Design and refine LLM prompts, context strategies, grounding approaches, structured outputs, guardrails, and validation steps
Integrate LLM capabilities with applications, APIs, data sources, identity services, and downstream systems
Support tool calling, workflow orchestration, human-in-the-loop controls, and exception handling for AI-enabled processes
Evaluate and monitor prompt performance, workflow outcomes, and production behavior
Design coexistence models for legacy, modern, and internal platforms during phased transformation
Support cutover sequencing, permissions validation, metadata mapping, data validation, and post-deployment stabilization
Identify integration risks and dependencies and coordinate mitigation across stakeholders
Partner with transformation Product Leads and Technical Program Managers to integrate Serval and enterprise platforms into operational processes
Provide technical leadership through design reviews, implementation guidance, troubleshooting, and engineering best practices
Author technical designs, dependency maps, data mappings, interface specifications, runbooks, and handover materials
Support validation, troubleshooting, hypercare, incident resolution, and early-adoption activities
Improve integration governance, support models, reusable patterns, and future delivery approaches

Requirements

What you’ll need

7+ years of hands-on experience in integration engineering, systems engineering, software engineering, platform architecture, or enterprise application integration within complex enterprise environments
Strong systems-thinking capability and experience mapping dependencies across applications, data layers, identity services, integrations, and business processes
Ability to independently lead complex integration initiatives and manage competing priorities in a highly matrixed organization
Bachelor’s degree in Computer Science, Information Technology, Engineering, Information Systems, or related technical field, or equivalent practical experience
Production-level proficiency in Python and core scripting or automation frameworks
Extensive experience building and supporting enterprise-grade integrations using REST APIs, webhooks, middleware solutions, connectors, event-driven services, and ETL or data-transformation logic
Strong understanding of cloud-based integration patterns, modern application architecture, Git-based version control, structured testing, and debugging across multi-system environments
Experience designing secure, supportable integrations across SaaS platforms, enterprise AI tools, Serval integrations, and internal enterprise systems
Experience with source-to-destination mapping, data transformation, schema management, validation, error handling, monitoring, and operational support
Extensive hands-on experience prompting large language models for business, technical, or automation use cases
Experience designing and evaluating prompt templates, context windows, grounding strategies, structured outputs, guardrails, and validation logic
Experience integrating LLMs with enterprise applications, APIs, tools, data sources, and workflow orchestration components
Familiarity with tool calling, retrieval-augmented generation, agent or workflow orchestration, human-in-the-loop processes, and LLM observability or evaluation
Ability to balance AI solution performance with security, privacy, reliability, explainability, cost, and supportability requirements
Strong knowledge of enterprise authentication, authorization, identity integration, access-control frameworks, and secure API practices
Experience partnering with Security, Privacy, Identity, ILM, Retention, and compliance stakeholders
Proven history of working with product managers, engineering teams, TPMs, security leads, and business stakeholders
Strong written and verbal communication skills, including explaining technical tradeoffs, architecture risks, data flows, and engineering decisions to nontechnical partners
GM does not provide immigration-related sponsorship; candidates must not require GM immigration sponsorship now or in the future

Benefits

Comp & perks

Hybrid work arrangement with expectation to report to a specific location at least 3 times a week (or frequency set by manager)
Potential eligibility for relocation benefits
GM Total Rewards benefits supporting employee well-being at work and home
Reasonable accommodations for applicants with disabilities