Senior AI Engineer

Motorola Solutions•Chicago, IL

34d•Hybrid

About The Position

The Senior AI Engineer will play a critical role in designing and deploying autonomous AI agents that enable multi-step reasoning, complex analysis, and optimization of supply chain processes. This position will lead the expansion of AI initiatives across the supply chain organization, with a particular focus on automation solutions that drive operational efficiency and data-driven decision-making. As a key member of the AI+BI team, you will work at the intersection of artificial intelligence, data engineering, and supply chain operations to create innovative solutions that transform how business users interact with data and automate complex supply chain processes.

Requirements

Proven track record of developing and deploying production AI/ML solutions
Experience working with supply chain, logistics, or operations data (preferred)
Deep understanding of LLM architectures (GPT, Claude, Llama, etc.) and their applications
Strong experience with agent design, tool-use, and multi-step reasoning frameworks
Experience with prompt engineering and conversational AI systems
Proficiency in fine-tuning and optimizing LLM models
Experience with AI/ML frameworks (LangChain, LlamaIndex, Hugging Face, TensorFlow, PyTorch)
Knowledge of model evaluation, testing, and performance optimization
Strong proficiency in Python (required)
Experience with API development (REST, GraphQL)
Familiarity with cloud platforms (Azure, AWS, or GCP)
Experience with version control (Git) and CI/CD practices
Knowledge of software engineering best practices and design patterns
Understanding of data engineering concepts (ETL/ELT, data pipelines)
Experience integrating AI systems with databases, data warehouses, and APIs
Familiarity with SQL and data querying
Knowledge of data security and governance practices
Experience with ChatGPT API, OpenAI, or similar LLM platforms
Knowledge of containerization (Docker, Kubernetes)
Experience with monitoring and observability tools
Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field
5+ years of professional experience in software engineering, AI/ML development, or data engineering
3+ years of hands-on experience with Large Language Models (LLMs) and conversational AI systems
2+ years of experience leading technical projects or initiatives

Nice To Haves

Advanced degree (Master's or PhD) in AI/ML, Computer Science, or related field
Experience in supply chain, logistics, or manufacturing industries
Experience with AWS Bedrock, Azure OpenAI, or Google Vertex AI
Knowledge of MLOps practices and model deployment pipelines
Experience with BI tools (Power BI, Tableau, Looker) and their integration with AI
Strong background in natural language processing (NLP) and information retrieval
Experience with agile development methodologies
Published research or contributions to open-source AI/ML projects
Certifications in cloud platforms or AI/ML technologies

Responsibilities

Design and Develop Autonomous AI Agents: Design, develop, and deploy autonomous AI agents capable of multi-step reasoning, planning, and tool use to execute complex supply chain tasks, including data analysis and process automation.
Develop ChatGPT for Data Experiences: Optimize conversational AI interfaces that enable natural language querying of supply chain data, including prompt engineering, data integration, and performance optimization
Execute AI Projects Across Supply Chain: Lead and execute AI initiatives across supply chain domains (procurement, logistics, inventory, forecasting, automation, etc.), from conception through deployment
Develop and Execute Solutions: As a senior engineer, take ownership of end-to-end solution development including architecture design, implementation, testing, and deployment of AI-powered supply chain solutions
Automation Development: Identify, design, and implement AI-driven automation solutions that optimize supply chain processes and drive operational efficiency
Data Integration: Build seamless connections between AI systems and underlying data infrastructure (data warehouses, APIs, databases, BI tools)
Stakeholder Collaboration: Work closely with supply chain business leaders and cross-functional teams to understand requirements, deliver value-driven solutions, and ensure successful adoption
Technical Leadership: Provide technical guidance, establish AI development best practices, and mentor junior engineers
Continuous Improvement: Stay current with AI/LLM advancements, evaluate new technologies, and continuously optimize existing solutions for performance and cost-effectiveness
Documentation & Knowledge Sharing: Create comprehensive technical documentation and share knowledge across the organization