Senior Agentic AI Engineer

Ingram Micro•Irvine, CA

1d•$112,200 - $190,700

About The Position

Accelerate your career. Join the organization that's driving the world's technology and shape the future. Ingram Micro is a leading technology company for the global information technology ecosystem. With the ability to reach nearly 90% of the global population, we play a vital role in the worldwide IT sales channel, bringing products and services from technology manufacturers and cloud providers to business-to-business technology experts. Our market reach, diverse solutions and services portfolio, and digital platform Ingram Micro Xvantage™ set us apart. Learn more at www.ingrammicro.com Come join our team where you’ll make technology happen in surprising ways. Let’s shape tomorrow - it’ll be a fun journey! Ingram Micro is a leading global IT distributor, connecting technology solution providers with vendors worldwide. We are at the forefront of leveraging cutting-edge technologies to drive innovation and efficiency across the IT ecosystem. Our AI Center of Excellence (CoE) is a dynamic and strategic group dedicated to solving complex business problems and creating new value streams through the application of Artificial Intelligence, with a strong focus on developing autonomous and intelligent Agentic AI systems. Your role: We are seeking a talented and motivated Agentic AI Engineer to join our AI CoE. In this hands-on role, you will be responsible for the design, development, testing, and deployment of AI agents that can perceive their environment, make decisions, and take autonomous actions. You will work closely with AI Architects, Data Scientists, and business stakeholders to build innovative agentic solutions that address key challenges in areas such as supply chain optimization, intelligent automation, and enhanced customer/vendor experiences. This is an exciting opportunity to contribute to the development of cutting-edge AI systems that will have a significant impact on our global operations.

Requirements

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Statistics, Machine Learning, or a related technical field. A PhD is a plus.
7+ years of hands-on experience in software engineering and/or data science, with a significant focus on building and deploying production-grade AI/ML systems.
Strong foundation in statistics, probability, and experimental design (e.g., A/B testing, causal inference).
Demonstrated expertise in architecting and developing complex AI systems, with deep, direct experience in Agentic AI or autonomous agents.
One or more agentic AI frameworks and libraries (e.g., Google's agentic stack including Vertex AI ADK, Agent Engine, Agent Builder, LangChain, AutoGen, CrewAI, LlamaIndex, Microsoft Semantic Kernel, or similar).
Advanced application of Large Language Models (LLMs) for complex reasoning, planning, and function calling (e.g., via APIs for GPT series, Claude, Gemini, Llama models).
Expert-level proficiency in Python and its ecosystem of AI/ML and data analysis libraries (e.g., scikit-learn, Pandas, NumPy, Hugging Face Transformers, PyTorch/TensorFlow, SciPy, StatsModels).
Deep understanding and application of advanced software engineering principles, architectural design patterns, and best practices (e.g., SOLID, version control with Git, CI/CD).
Extensive experience with API design, development, and integration (RESTful APIs, gRPC).
Deep experience with cloud computing platforms (e.g., GCP, AWS, Azure) and designing solutions using their AI/ML services (e.g., Google Vertex AI, Amazon SageMaker).
Exceptional problem-solving, analytical, and critical thinking skills.
Excellent verbal and written communication skills, with the ability to articulate complex technical and statistical concepts to both technical and non-technical audiences.

Nice To Haves

Extensive experience building, deploying, and maintaining high-availability AI solutions in a large-scale production environment.
Experience with advanced statistical modeling techniques (e.g., causal inference, time series analysis, Bayesian methods).
Experience with large-scale data processing and analysis frameworks (e.g., Spark, Dask).
Expertise in designing and implementing RAG systems, including deep knowledge of vector databases.
Contributions to open-source AI/ML projects or publications in relevant academic conferences.
Experience in strategic business domains such as supply chain, finance, logistics, or enterprise automation.

Responsibilities

Lead the end-to-end design, development, and deployment of complex, scalable, and secure enterprise-grade AI agents.
Define the architectural patterns and technical standards for agentic systems across the organization.
Architect and oversee the integration of AI agents with critical enterprise systems (ERP, CRM, SCM, WMS), ensuring robust data flows and operational efficiency.
Drive the development and implementation of advanced agent functionalities, including sophisticated reasoning chains, multi-agent collaboration, long-term memory, and dynamic tool creation.
Champion and enforce standards for clean, highly efficient, well-documented, and rigorously tested code.
Mentor junior and mid-level engineers in advanced programming techniques and best practices.
Partner with AI Architects and business stakeholders to translate strategic objectives and architectural blueprints into tangible, high-impact AI solutions.
Act as a key technical leader in collaborations with Data Scientists, Platform Engineers, and Product Managers, guiding the analytical approach and ensuring the rigorous application of statistical methods in AI/ML models.
Lead the ideation and development of innovative prototypes and proof-of-concepts to demonstrate the art of the possible, de-risk new technologies, and validate complex analytical approaches.
Guide and improve agile development practices within the team, enhancing velocity and outcomes in sprint planning, stand-ups, and reviews.
Design and implement comprehensive A/B testing and experimentation frameworks to validate agent performance and drive data-driven decisions.
Establish rigorous statistical benchmarks for model reliability, accuracy, and business impact.
Take ownership of the analytical aspects of the model lifecycle, from feature engineering and selection to post-deployment performance analysis.
Conduct deep-dive analyses to understand model behavior and identify opportunities for improvement.
Serve as the escalation point for complex issues related to model performance and data drift.
Utilize advanced statistical analysis to diagnose root causes and implement robust, long-term solutions.
Stay at the forefront of advancements in agentic AI, LLMs, and statistical learning theory, and act as a subject matter expert, evangelizing new analytical techniques and methodologies within the AI CoE.
Drive the continuous improvement and definition of the CoE's agent development processes, analytical tools, and validation standards.
Actively mentor and coach junior and mid-level AI Engineers and Data Scientists, fostering their technical growth and contributions to the team.