Engineering Manager
Leads product strategy and development for AI-driven public safety and intelligence solutions, overseeing data integration, machine‑learning models, and cloud‑based platforms to enable smarter law‑enforcement and emergency response tools.
Company Overview
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.
Department Overview
Job Description
As the Director of Product, you will own the vision, strategy, and execution of our new, high-impact intelligence portfolio. We need an AI-native thinker with deep technical awareness of data architecture, cross-platform interoperability, and modern APIs.
Crucially, you will lead the evolution from passive data tools to active automation by defining and deploying autonomous AI agents capable of executing complex knowledge work for public safety professionals. Because your users will span from Command Staff and Chiefs making macro decisions, to active supervisors, down to frontline responders facing high-stress situations, a relentless focus on intuitive, mission-critical User Experience (UX) is paramount.
Key Responsibilities
Product Vision & Strategy: Own the end-to-end lifecycle of the new intelligence suite, positioning it as the premier alternative to market incumbents.
AI Agent Orchestration: Define the functional frameworks, safety boundaries, and task-flows for autonomous AI agents capable of automating deep knowledge work and multi-system data synthesis.
Cross-Platform Data Integration: Define requirements for robust data bridges, APIs, and ingestion pipelines that cleanly unify data across legacy on-premise infrastructure and modern cloud platforms.
AI-Native Implementation: Drive product workflows using advanced machine learning capabilities, including automated report summarization, natural language querying, entity resolution, and anomaly detection.
Trust & Compliance Governance: Ensure all analytical models and AI agents strictly adhere to CJIS requirements and rigorous data privacy boundaries, maintaining explicit "human-in-the-loop" verification mechanisms.
Technical Awareness & The "AI-Native" Mindset
Agentic Knowledge Work: You know how multi-agent systems coordinate, break down multi-step tasks, self-correct, and navigate permissions to execute research for human users.
Data Bridging: You understand ETL/ELT pipelines, webhook-driven events, and API constraints, knowing how to query unstructured data from a 20-year-old database just as smoothly as a modern cloud feed.
Semantic Ontologies: You naturally look at complex data and think about how a semantic ontology or graph database can map relationships dynamically across s
Posted June 25, 2026