Senior Applied AI Engineer – Supply Chain

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.

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.

• 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.

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

Target Base Salary Range: $75,000  - $125,000 USD

Consistent with Motorola Solutions values and applicable law, we provide the following information to promote pay transparency and equity. Pay within this range varies and depends on job-related knowledge, skills, and experience. The actual offer will be based on the individual candidate.

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• 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.
• Education: Bachelor's Degree in Computer Science, Artificial Intelligence or related