Senior Applied AI Engineer, Motorola Solutions

• Experience: 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
• Programming & Systems: Expert-level Python and SQL. Deep understanding of distributed systems, microservices, and modern cloud infrastructure (AWS/GCP/Azure)
• Generative AI Expertise: Mastery of LLM orchestration frameworks (LangChain, LangGraph, AutoGen, CrewAI), vector databases (e.g., Pinecone, Weaviate), and retrieval optimization techniques (hybrid search, semantic routing)
• Production Engineering: Extensive experience with containerization (Docker, Kubernetes), CI/CD pipelines, API design, and system observability tools
• Communication: Proven ability to influence product roadmaps, manage technical debt, and communicate complex AI constraints and capabilities to non-technical business leaders
• Data & Integration: 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
• Infrastructure: Knowledge of containerization (Docker, Kubernetes) and cloud-native architecture on AWS

Desirable

• 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
• Enterprise LLM Architecture: 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
• Advanced Multi-Agent Systems: 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
• Real-Time Event Orchestration: Build highly available, event-driven data streaming pipelines that power proactive alerting systems, detecting supply chain anomalies and inventory shortages before they impact operations
• Evaluate & Evolve Agentic Architectures: 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)
• Technical Leadership & Mentorship: Mentor junior and mid-level engineers, establish coding and MLOps standards, conduct architectural reviews, and guide the team’s overall technical strategy
• Build Autonomous Agents: 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
• Drive Proactive Automation: 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
• AI Evaluation & Benchmarking: Design end-to-end AI evaluation strategies, leveraging Langfuse for experiment tracking and dataset management to run systematic benchmarks and drive continuous model improvements
• Data & API Integration: Build seamless connections between our AI agentic systems and underlying data warehouses, BI platforms, and operational APIs
• Technical Leadership: Provide architectural guidance, establish LLMOps and agentic development best practices, and mentor junior engineers on the team
• Stakeholder Collaboration: Work closely with supply chain business leaders to understand requirements, translate them into technical architectures, and ensure successful adoption of automation tools