AI Agent Engineer

Job Description

The Role

As an AI Agent Engineer, you will design, build, and scale enterprise integrations and data pipelines that modernize how systems work together across General Motors. From the software and core IT side of the transformation, you will focus on connecting Serval integrations, SaaS platforms, enterprise AI tools, and GM’s core business systems through secure, reusable, and scalable integration patterns.

This role is a strong fit for an experienced engineer who can independently deliver complex integration solutions, translate business and technical requirements into practical system designs, and build supportable, production-ready pipelines that align with enterprise standards. You will partner closely with product, engineering, technical program management, security, and business stakeholders to enable reliable data flow, workflow continuity, and platform interoperability across GM, while contributing reusable patterns that can be adopted broadly across the enterprise.

What You’ll Do

Strategic architecture and standards alignment

• Build reusable services, accelerators, and reference integration templates so transformation teams can solve repeatable connectivity needs more consistently.

• Apply enterprise platform guardrails by aligning integration designs with data standards, identity guidelines, security policies, and operational requirements.

• Partner with Security, Privacy, Identity, Information Lifecycle Management (ILM), and Retention teams to support workflows that process sensitive or regulated data.

• Evaluate incoming integration requests and recommend the appropriate technical path using approved platform capabilities, configuration options, or custom engineering.

• Define integration architectures that account for system boundaries, data ownership, reliability, observability, failure handling, and long-term sustainment.

Integration delivery and pipeline engineering

• Design, build, and maintain robust, resilient, and observable integrations using APIs, webhooks, scripts, connectors, middleware services, and orchestration logic.

• Define source-to-destination data mappings, transformation logic, exception-handling patterns, validation rules, and monitoring approaches that support reliable day-to-day operations.

• Modernize legacy interfaces, manually routed workflows, platform-specific automations, and older integrations into scalable, API-driven, and event-based integration patterns.

• Connect Serval integrations with core GM platforms, including 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 integration failures across multi-system environments and improve performance, reliability, and supportability.

LLM prompting and AI-enabled integration

• Design, test, and refine prompts for large language models used within enterprise workflows and integration solutions.

• Develop prompt templates, context strategies, grounding approaches, structured outputs, guardrails, and validation steps that improve accuracy, consistency, and reliability.

• Integrate LLM capabilities with enterprise applications, APIs, data sources, identity services, and downstream systems while addressing security, privacy, latency, and failure-handling requirements.

• Support tool calling, workflow orchestration, human-in-the-loop controls, and exception handling for AI-enabled processes.

• Apply practical evaluation and monitoring approaches to assess prompt performance, workflow outcomes, and production behavior.

Migration and coexistence engineering

• Design and support coexistence models that allow legacy, modern, and internal platforms to interoperate effectively during phased transformation waves.

• Preserve workflow continuity where historical data, inactive-user content, or core systems must remain on legacy platforms during transition.

• Support cutover sequencing, permissions validation, metadata mapping, data validation, and post-deployment stabilization to reduce operational disruption.

• Identify integration risks and dependencies early and coordinate mitigation across technical and business stakeholders.

Transformation pod engagement and sustainment

• Partner directly with transformation Product Leads and Technical Program Managers within business units to integrate Serval capabilities and enterprise platforms into real-world operational processes.

• Provide technical leadership through design reviews, implementation guidance, troubleshooting, and engineering best practices across the program.

• Author technical designs, dependency maps, data mappings, interface specifications, operational runbooks, and handover materials that support long-term sustainment of integrated solutions.

• Support validation, troubleshooting, hypercare, incident resolution, and early-adoption activities during rollout.

• Contribute practical feedback that improves integration governance, support models, reusable patterns, and future delivery approaches.

Your Skills & Abilities (Required Qualifications)

Core experience and education

• 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, with proven experience mapping dependencies across applications, data layers, identity services, integrations, and business processes.

• Proven ability to independently lead complex integration initiatives and manage competing priorities across a highly matrixed organization.

• Bachelor’s degree in Computer Science, Information Technology, Engineering, Information Systems, or a related technical field, or equivalent practical experience.

Integration and software engineering proficiency

• Strong 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 integration issues 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.

LLM prompting and intelligent automation

• 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.

Security, identity, and cross-functional delivery

• Strong foundational 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 on integration designs and risk remediation.

• Proven history of working effectively with product managers, engineering teams, TPMs, security leads, and business stakeholders.

• Strong written and verbal communication skills, including the ability to explain technical tradeoffs, architecture risks, data flows, and engineering decisions to nontechnical partners.

What Will Give You a Competitive Edge (Preferred Qualifications)

• Direct experience supporting Serval integrations or comparable enterprise integration and orchestration platforms.

• Experience connecting enterprise workflows to modern data and AI ecosystems, including LLM platforms, enterprise search, automation tools, and data platforms.

• Familiarity with corporate data retention, ILM, privacy frameworks, and regulated data controls in large-scale enterprise environments.

• Experience supporting automated testing, cutover planning, data validation, hypercare support, and long-term sustainment models.

• Experience designing reusable integration frameworks, reference architectures, templates, accelerators, and operational standards.

• Familiarity with event-driven architecture, message-based integration, middleware, API management, data pipelines, and cloud-native services.

• Experience leading modernization, migration, transformation, or coexistence initiatives involving multiple enterprise platforms.

• Relevant cloud, integration, systems engineering, cybersecurity, or AI certifications, or equivalent practical experience.



GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc).

This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.

This job may be eligible for relocation benefits.



About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

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