Principal, Agentic AI Engineer

Ingram Micro•Irvine, CA

1d•$133,400 - $226,800

About The Position

Ingram Micro is a leading technology company for the global information technology ecosystem. We are seeking a highly experienced and innovative Agentic AI Architect to join our AI CoE. In this role, you will be a primary technical authority responsible for designing the foundational architecture and engineering blueprints for our next-generation AI agent ecosystems. You will translate strategic AI objectives into robust, scalable, and secure technical designs, ensuring our agentic solutions effectively address complex business challenges in areas such as supply chain optimization, intelligent automation, and enhanced customer/vendor experiences. As a Principal Engineer, you will provide deep technical guidance, mentor other engineers, and champion architectural best practices for building cutting-edge agentic AI systems.

Requirements

Master’s or Ph.D. in Computer Science, Artificial Intelligence, Software Engineering, or a closely related technical field.
10+ years of experience in software engineering and AI/ML system development, with at least 5-7 years focused on architecting and designing complex, distributed AI systems.
Deep, hands-on expertise in architecting and implementing Agentic AI systems or autonomous intelligent agents.
Practical experience with designing architectures for systems utilizing agentic frameworks and libraries (e.g., LangChain, AutoGen, CrewAI, LlamaIndex, Microsoft Semantic Kernel, Google's agentic stack including Vertex AI Search and Conversation, Agent Builder, or similar).
Architecting solutions that leverage Large Language Models (LLMs) for core agent functionalities like reasoning, planning, and complex interaction (e.g., GPT series, Claude, Gemini, Llama models), including strategies for prompt management, context handling, RAG, and robust function calling.
Strong proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers).
Expert understanding of software architecture principles, design patterns (e.g., microservices, event-driven architecture, SOA), APIs, and distributed systems engineering, with a proven ability to apply them to AI/agentic systems.
Solid experience with MLOps/LLMOps practices and tools for model/agent lifecycle management, and a clear vision for how these apply to agentic AI.
Significant experience with data architecture, data engineering, data pipelines, vector databases, and knowledge graphs as they pertain to enabling intelligent agents.
In-depth knowledge of cloud computing platforms (e.g., GCP, AWS, Azure) and experience designing AI solutions on these platforms.
Proven ability to lead the architectural design of complex technical projects and make critical technology choices.
Exceptional analytical, problem-solving, and system-thinking skills.
Strong communication and collaboration skills, with the ability to influence and guide technical teams.

Nice To Haves

Demonstrated experience architecting agentic AI solutions for sales automation, customer service, or vendor management, supply chain management, logistics or inventory optimization ideally within the IT distribution or a similar complex B2B industry.
Expertise in multi-agent systems (MAS) architecture, inter-agent communication protocols, and coordination strategies.
Experience architecting systems incorporating reinforcement learning (RL).
Familiarity with designing and implementing simulation environments for testing and validating complex agent behaviors.
Deep understanding of security architecture principles as applied to AI and autonomous systems.
Track record of defining and driving the adoption of new architectural standards or technology platforms within an organization.
Experience contributing to or leading technical governance bodies (e.g., Architecture Review Boards).

Responsibilities

Lead the definition and design of the end-to-end reference architecture for enterprise-grade agentic AI systems, encompassing agent lifecycle management, interaction protocols, knowledge integration, and action execution frameworks.
Develop detailed architectural blueprints, patterns, and standards for building, deploying, and integrating AI agents across diverse business domains and existing enterprise platforms (ERP, CRM, SCM, WMS).
Design core components of the agentic ecosystem, including reasoning engines (leveraging LLMs), planning modules, perception interfaces, memory systems, and tool/API integration layers.
Ensure architectural designs meet critical non-functional requirements, including scalability, reliability, security, maintainability, and cost-effectiveness.
Drive the technical evaluation and selection of appropriate frameworks (e.g., Gogole ADK, LangChain, AutoGen, CrewAI), platforms, and tools for our agentic AI stack.
Serve as a senior technical expert and thought leader on agentic AI architecture, multi-agent systems, LLM-driven autonomy, and related technologies.
Lead the development of advanced prototypes and proof-of-concepts for core architectural components and novel agentic capabilities to validate design choices and mitigate technical risks.
Provide expert technical guidance and architectural oversight to development teams working on specific AI agent projects.
Collaborate with the Agentic AI Lead and other stakeholders to align architectural decisions with the overall AI strategy and roadmap.
Establish and promote engineering best practices, coding standards, and design patterns for agentic AI development within the CoE.
Contribute to the development of Agentic AI Tech Ops/MLOps/LLMOps strategies specifically tailored for the lifecycle management of AI agents.
Ensure that architectural designs and implementations adhere to ethical AI principles (fairness, transparency, accountability, privacy) and relevant compliance requirements.
Create and maintain comprehensive architectural documentation.
Mentor and coach AI engineers and developers on advanced architectural concepts, design principles, and new technologies in the agentic AI space.
Foster a culture of technical excellence, innovation, and knowledge sharing.
Act as an internal evangelist for sound architectural practices in AI development.
Work closely with data scientists, data engineers, security architects, infrastructure teams, and business stakeholders to ensure holistic and well-integrated agentic solutions.
Translate complex business requirements and domain-specific challenges into effective and scalable architectural designs.
Clearly articulate and defend architectural decisions to both technical and non-technical audiences.