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
UC Unified User Experience Engineering
Job Description
Role Summary: We are seeking a highly skilled and hands-on AI Engineer to build, deploy, and optimize enterprise-grade Artificial Intelligence solutions. This role also requires a commercially minded AI specialist to discover, validate, and integrate enterprise-grade Artificial Intelligence technologies. This role is pivotal in developing AI features for our customer-facing products (Cloud platforms and push-to-talk ecosystems) and automating our internal technical operations. The ideal candidate is a strong software engineer with deep expertise in machine learning, capable of discovering and taking models from proof-of-concept to production while ensuring low latency, high reliability, and efficient compute utilization at both the edge and the cloud.
Core Responsibilities
1. AI Model Development & Implementation
Build & Deploy: Develop, fine-tune, and deploy machine learning models (e.g., robust audio processing, NLP, and GenAI) into production environments.
Audio & Speech AI: Implement advanced audio features using edge-to-cloud ML models. Focus on coding real-time noise suppression, acoustic anomaly detection, and speech-to-text (ASR) pipelines optimized for high-noise environments.
Dispatch Intelligence: Develop the backend logic and prompt engineering for cloud dispatch features, including automated radio chatter summarization and intelligent keyword alerting.
2. Architecture & Edge-to-Cloud Integration
Edge Optimization: Optimize models for deployment on resource-constrained, ruggedized Push-to-Talk (PTT) devices. Utilize frameworks like ONNX, TensorRT, or TensorFlow Lite to balance latency, battery life, and accuracy.
Cloud AI Services: Integrate and manage AI workloads across major cloud providers (AWS, Azure, or GCP), writing scalable APIs and microservices to support AI features.
RAG System Development: Build and maintain secure, Retrieval-Augmented Generation (RAG) pipelines to power internal knowledge assistants for IT admins and support dispatchers.
3. MLOps & Operational Reliability
Pipeline Automation: Design and maintain CI/CD pipelines for machine learning models. Automate model training, testing, and deployment processes.
Telemetry & Monitoring: Implement robust monitoring solutions to track model performance, data drift, and latency in real-time, proactively mitigating degradation in zero-fault tolerance communication environments.
Internal Tooling: Leverage AI APIs to build internal scripts and tools that assist with code reviews, automated QA test generation, and predictive cloud infrastructure maintenance.
Basic Requirements
Experience & Mindset
Experience: 3+ years of hands-on experience as an Machine Learning Engineer, AI Developer, or Software Engineer with a strong focus on applied AI, ideally within telecommunications, UCaaS, or cloud SaaS.
Engineering Excellence: Deep commitment to writing clean, scalable, and maintainable production code. Experience deploying software in high-availability, mission-critical environments.
Problem Solver: Ability to take a high-level strategic requirement and translate it into a concrete, functioning software solution.
Technical Competencies
Programming Languages: Expert-level proficiency in Python . Strong working knowledge of C++ (highly preferred for edge deployment and audio processing).
AI/ML Frameworks: Deep experience with PyTorch, TensorFlow, or JAX. Familiarity with LLM orchestration…