AI Agent Engineer

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must only include sections that have real specific content. The posting includes a description: "Job Description The Role ..." then sections like "What You’ll Do" (Responsibilities), "Your Skills & Abilities (Required Qualifications)" (Qualifications/Skills), "What Will Give You a Competitive Edge (Preferred Qualifications)" (maybe Skills/Qualifications), "GM does not provide immigration-related sponsorship..." (metadata), "This role is categorized as hybrid..." (Schedule maybe), "About GM" (About the role? maybe About the company), "Why Join Us" (maybe Benefits?), "Benefits Overview" (Benefits), "Non-Discrimination..." (EEO boilerplate to remove), "Accommodations" (maybe not needed). We must remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. Also remove immigration sponsorship note? It's not boilerplate? It's a specific note about sponsorship; but it's not a heading; we can fold minor metadata into a short

or drop it. Probably we can include a short paragraph about sponsorship? The instruction: "Do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short

or drop it." The sponsorship note is minor metadata; we could drop it. Also hybrid schedule note: "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." That's schedule info; we can include a Schedule section with that content. Benefits Overview: we have a Benefits Overview paragraph; we can include Benefits section with that content. Pay: not mentioned; so omit Pay section. About the role: we have "About GM" and "Why Join Us". Could combine into About the role? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to decide which sections to include based on content. We have: - About the role: maybe "The Role" paragraph at start describing the role. That's suitable for About the role. - Responsibilities: "What You’ll Do" with subparts (Strategic architecture..., Integration delivery..., LLM prompting..., Migration..., Transformation pod engagement...). That's responsibilities. - Requirements/Qualifications/Skills: The posting has "Your Skills & Abilities (Required Qualifications)" with subsections: Core experience and education, Integration and software engineering proficiency, LLM prompting and intelligent automation, Security, identity, and cross-functional delivery. That's qualifications/skills. Also "What Will Give You a Competitive Edge (Preferred Qualifications)" could be additional qualifications/skills; we could merge into Skills or Qualifications. - Benefits: "Benefits Overview" paragraph. - Schedule: hybrid note. - About GM: maybe part of About the role? Could be separate "About GM" but not in list; but we could include as part of About the role or maybe as a separate section like "About the Company". Not in allowed headings list; but we could still include if we think it's substantive. The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but suggests those as examples. Safer to stick to those headings. So we can put About the role covering the initial description and maybe About GM and Why Join Us? But Why Join Us is more about culture/benefits; maybe could go under Benefits? But Benefits Overview already covers benefits. Why Join Us is more about culture; could be part of About the role. We'll include it in About the role. We must not repeat the job title as a heading. The job title is "AI Agent Engineer". Not to be used as heading. We must preserve all substantive information; do not invent or summarize away detail. So we need to keep the details from each section. We need to use

• for any list of duties/requirements/benefits, and

  for prose. We need to parse the responsibilities into bullet lists. The responsibilities have headings: Strategic architecture and standards alignment, Integration delivery and pipeline engineering, LLM prompting and AI-enabled integration, Migration and coexistence engineering, Transformation pod engagement and sustainment. Under each there are sentences. We could treat each heading as a subsection? But we only have

  for main sections; we could embed

  maybe but not required. Simpler: we can make a single

  for responsibilities with each bullet containing the detailed sentences (maybe multiple sentences). That would preserve info but lose subheadings. However we could embed or

  inside

  . The instruction didn't forbid other tags; just said use

  for section headings,

  • for any list,

    for prose. So we can have

    Responsibilities then

    • each bullet maybe with subheading. That's acceptable. Similarly for qualifications: we have subsections; we can make a list. For preferred qualifications: also list. For benefits: we have a paragraph; we can put

      . For schedule: we have hybrid note; we can put

      . We must not include boilerplate: Non-Discrimination, Equal Employment Opportunities, Accommodations, etc. Also remove "GM does not provide immigration-related sponsorship..." and maybe the hybrid note we keep as schedule. Also remove "About GM" maybe we incorporate into About the role. Let's extract content. **About the role**: The initial paragraph after "Job Description The Role". Actually the text: "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." We'll put that as

      under

      About the role

      . **Responsibilities**: "What You’ll Do" with subparts. Let's capture each bullet: 1. 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. 2. 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. 3. 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. 4. 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. 5. 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. We'll put these as
      • each bullet maybe with subheading. **Requirements / Qualifications / Skills**: "Your Skills & Abilities (Required Qualifications)" with subsections. Let's extract each bullet. 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