As an AI Agent Engineer, you will join the software and core IT team to design, build, and scale enterprise integrations and data pipelines that modernize system connectivity across General Motors. You will develop robust, production-ready integration patterns by connecting Serval integrations, SaaS platforms, and enterprise AI tools with core business systems. Your daily work involves building reusable services, designing source-to-destination data mappings, and creating automated workflows using Python, REST APIs, webhooks, and middleware. Additionally, you will focus on LLM prompting and intelligent automation, where you will design prompt templates, grounding strategies, and evaluation logic to integrate large language models into enterprise applications. This role solves the complex challenge of ensuring seamless data flow and platform interoperability while navigating security, identity management, and the transition from legacy systems to modern, scalable, API-driven architectures.
Design and build scalable data pipelines and integration patterns using APIs, webhooks, and middleware services.
Develop reusable service templates and reference architectures to standardize connectivity across enterprise systems.
Create and refine prompt engineering strategies for large language models to improve accuracy in automated workflows.
Integrate LLM capabilities with internal applications while ensuring compliance with security and privacy guardrails.
Modernize legacy interfaces into API-driven, event-based integration patterns to ensure seamless system interoperability.
Map data transformations, validation rules, and error-handling logic for reliable production-ready integrations.
Manage the coexistence of old and new systems during phased technology transitions to maintain workflow continuity.
Produce technical documentation including design maps, interface specifications, and operational runbooks for long-term support.
What we're looking for
Bachelor’s degree in Computer Science, Information Technology, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
7+ years of hands-on experience in integration engineering, systems engineering, software engineering, platform architecture, or enterprise application integration.
Production-level proficiency in Python and core scripting or automation frameworks.
Extensive experience building enterprise-grade integrations using REST APIs, webhooks, middleware, event-driven services, and ETL logic.
Experience designing and evaluating prompt templates, context windows, grounding strategies, and validation logic for large language models.
Experience integrating LLMs with enterprise applications, APIs, data sources, and workflow orchestration components.
Strong knowledge of enterprise authentication, authorization, identity integration, and secure API practices.
Experience supporting Serval integrations or comparable platforms (preferred); relevant cloud, integration, systems engineering, cybersecurity, or AI certifications (preferred).