Senior Agentic AI Engineer

Accelerate your career by joining Ingram Micro, a leading global IT distributor that connects technology solution providers with vendors worldwide. With a reach of nearly 90% of the global population, we play a vital role in the IT sales channel, delivering products and services from technology manufacturers and cloud providers to business-to-business technology experts. Our diverse solutions portfolio, market reach, and digital platform Ingram Micro Xvantage™ set us apart. Shape the future of technology with us—it’ll be a fun journey!

About the role

We are seeking a talented and motivated Agentic AI Engineer to join our AI Center of Excellence (CoE). In this hands-on role, you will design, develop, test, and deploy AI agents capable of perceiving their environment, making decisions, and taking autonomous actions. You will collaborate with AI Architects, Data Scientists, and business stakeholders to build innovative agentic solutions addressing challenges in supply chain optimization, intelligent automation, and enhanced customer/vendor experiences. This is an opportunity to contribute to cutting-edge AI systems with a significant impact on our global operations.

Responsibilities

• AI Strategy & Technical Leadership:
  • Lead the end-to-end design, development, and deployment of complex, scalable, and secure enterprise-grade AI agents.
  • Define architectural patterns and technical standards for agentic systems across the organization.
• Strategic System Integration:
  • Architect and oversee the integration of AI agents with critical enterprise systems (ERP, CRM, SCM, WMS), ensuring robust data flows and operational efficiency.
• Own Core Functionality & Innovation:
  • Drive the development and implementation of advanced agent functionalities, including sophisticated reasoning chains, multi-agent collaboration, long-term memory, and dynamic tool creation.
• Code Excellence & Mentorship:
  • 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.
• Collaboration & Influence:
  • Architectural Partnership: Partner with AI Architects and business stakeholders to translate strategic objectives and architectural blueprints into tangible, high-impact AI solutions.
  • Cross-Functional Leadership: 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.
• Drive Prototyping & Feasibility:
  • 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.
• Agile Process Improvement:
  • Guide and improve agile development practices within the team, enhancing velocity and outcomes in sprint planning, stand-ups, and reviews.
• Advanced Analytics & Model Performance:
  • Lead Experimentation & Validation Strategy: 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.
  • Own Model Lifecycle & Analysis: 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.
  • Advanced Troubleshooting & Root Cause Analysis: 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.
• Mentorship & Thought Leadership:
  • Continuous Learning & Evangelism: 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.
  • Process & Standards Definition: Drive the continuous improvement and definition of the CoE's agent development processes, analytical tools, and validation standards.
  • Mentor & Develop Talent: Actively mentor and coach junior and mid-level AI Engineers and Data Scientists, fostering their technical growth and contributions to the team.

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, including mastery of:
  • 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.

Preferred Qualifications

• 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.

Benefits

• Be a key contributor to building innovative Agentic AI solutions that shape the future of a major global IT distributor.
• Gain hands-on experience with cutting-edge AI technologies and frameworks.
• Work in a dynamic, collaborative, and supportive team environment focused on learning and growth.
• Opportunity to make a tangible impact on real-world business problems.
• Competitive salary, bonus, and benefits package.
• Access to healthcare benefits, paid time off, parental leave, a 401(k) plan with company match, short-term and long-term disability coverage, basic life insurance, and wellbeing benefits.

Pay

The typical base pay range for this role across the U.S. is USD $112,200.00 - $190,700.00 per year. The range reflects the potential annual base pay for all roles; the applicable range will depend on the candidate’s primary work location, pay grade, and variable compensation plan. Individual base pay within each range depends on factors such as role complexity, job duties, relevant experience, and skills. Base pay ranges are reviewed and typically updated each year. Offers are made within the applicable base pay range at the time of hire, with new hires generally starting in the bottom half (between the minimum and midpoint) of the range. Certain roles are eligible for additional rewards, including merit increases, annual bonuses, sales incentives, and long-term incentives, allocated based on position level and individual performance.