Senior Applied AI Engineer – Supply Chain

Motorola Solutions•Chicago, IL

8d•Hybrid

About The Position

At Motorola Solutions, we believe that everything starts with our people. We’re a global close-knit community, united by the relentless pursuit to help keep people safer everywhere. We build and connect technologies to help protect people, property and places. Our solutions foster the collaboration that’s critical for safer communities, safer schools, safer hospitals, safer businesses, and ultimately, safer nations. Connect with a career that matters, and help us build a safer future. The Supply Chain AI & Data team drives the strategic transformation of our operations in alignment with Vision 2030. We are building the next evolution of data on AI—moving beyond traditional analytics to architect an intelligent, autonomous supply chain ecosystem. As the strategic engine behind critical initiatives like Project Falcon, our mission is to fundamentally redefine how the business operates, turning complex logistical challenges into proactive, resilient, and forward-looking capabilities. As a Senior Applied AI Engineer on our Supply Chain team, you will drive the technical vision and architectural design for integrating foundational models and autonomous systems into our global logistics network. You will own the end-to-end lifecycle of enterprise-grade AI products—evolving conversational interfaces into highly reliable, multi-agent systems that execute complex supply chain decisions in real time. Beyond writing production code, you will architect secure, scalable LLM deployments, design rigorous evaluation frameworks, and mentor junior engineers. You will act as a strategic partner to supply chain leadership, translating massive operational bottlenecks into deployed, autonomous AI solutions.

Requirements

5+ years of software engineering, data engineering, or ML engineering experience, with a proven track record of architecting and deploying LLMs or AI agents into production environments.
Expert-level Python and SQL.
Deep understanding of distributed systems, microservices, and modern cloud infrastructure (AWS/GCP/Azure).
Mastery of LLM orchestration frameworks (LangChain, LangGraph, AutoGen, CrewAI), vector databases (e.g., Pinecone, Weaviate), and retrieval optimization techniques (hybrid search, semantic routing).
Extensive experience with containerization (Docker, Kubernetes), CI/CD pipelines, API design, and system observability tools.
Proven ability to influence product roadmaps, manage technical debt, and communicate complex AI constraints and capabilities to non-technical business leaders.
Experience with BI automation, Text-to-SQL workflows, and programmatically generating visualizations or dashboards via API (e.g., Power BI, Tableau, Looker, custom frameworks).
Familiarity with ETL/ELT pipelines and SQL.
Knowledge of containerization (Docker, Kubernetes) and cloud-native architecture on AWS.
Bachelor's Degree in Computer Science, Artificial Intelligence or related

Nice To Haves

Deep working knowledge of enterprise supply chain dynamics, ERP architecture (e.g., SAP, Oracle), procurement, or logistics optimization.
Experience building custom evaluation frameworks (e.g., Ragas) or fine-tuning open-source foundation models for domain-specific tasks.
High proficiency with Kafka, Spark, or similar technologies to support real-time decision engines.

Responsibilities

Architect, deploy, and scale robust Conversational AI applications. Own the system design to ensure low latency, high accuracy, and strict data governance across proprietary ERP and warehouse data.
Lead the development of sophisticated agentic workflows capable of taking independent action (e.g., auto-generating purchase orders, dynamically rerouting freight). Design secure execution environments and rigorous Human-in-the-Loop (HITL) fallback mechanisms for high-stakes decisions.
Build highly available, event-driven data streaming pipelines that power proactive alerting systems, detecting supply chain anomalies and inventory shortages before they impact operations.
Take lead technical ownership of our existing GenAI data platform (AWS Bedrock, Langfuse), and lead the evaluation, selection, and migration to a next-generation agentic orchestration framework (e.g., LangGraph, CrewAI, AutoGen, Semantic Kernel).
Mentor junior and mid-level engineers, establish coding and MLOps standards, conduct architectural reviews, and guide the team’s overall technical strategy.
Design, develop, and deploy semi- and fully autonomous AI agents capable of planning, tool-use, and executing complex supply chain tasks with minimal human intervention.
Evolve the current conversational interface into a proactive engine that actively monitors supply chain data, triggers intelligent alerts, and automatically generates dynamic dashboards for business users.
Design end-to-end AI evaluation strategies, leveraging Langfuse for experiment tracking and dataset management to run systematic benchmarks and drive continuous model improvements.
Build seamless connections between our AI agentic systems and underlying data warehouses, BI platforms, and operational APIs.
Provide architectural guidance, establish LLMOps and agentic development best practices, and mentor junior engineers on the team.
Work closely with supply chain business leaders to understand requirements, translate them into technical architectures, and ensure successful adoption of automation tools.