GenAI Engineering Manager

Motorola Solutions•Richardson, TX

1d•$150,000 - $175,000

About The Position

Motorola Solutions is seeking a GenAI Engineering Manager to oversee the design, development, and deployment of Digital workflows using GenAI LLM models and applications. This role involves defining and driving the product roadmap for Gen AI capabilities within a wearable voice platform, from intent recognition to real-time AI agent orchestration. The successful candidate will design AI behaviors that can reason, recall, and act autonomously in response to multi-turn, real-world voice interactions. This includes architecting and implementing end-to-end generative AI pipelines covering speech-to-text (ASR), LLM-based reasoning, retrieval-augmented generation (RAG), and text-to-speech (TTS). The role also entails leading the development of context-aware, multimodal AI agents capable of real-time voice interaction, memory retention, and adaptive decision-making, as well as enabling intelligent agent orchestration across devices and environments. Collaboration with cross-functional teams (product, design, data science) is essential for defining project scopes and deliverables, and the manager will drive end-to-end project execution, from research exploration to cost and LLM model selection.

Requirements

Bachelor’s or Master's degree in Computer Science or a related field.
At least 8+ years of experience in AI/ML, with at least 2 years in a leadership or managerial role.
Experience managing teams in software development environment
Excellent communication skills both written and verbal
Strong analytical and problem-solving abilities
Proven track record of success working in complex environments

Responsibilities

Oversee the design, development, and deployment of Digital workflows using GenAI LLM models and applications.
Define and drive the product roadmap for Gen AI capabilities within a wearable voice platform, from intent recognition to real-time AI agent orchestration.
Design AI behaviors that can reason, recall, and act autonomously in response to multi-turn, real-world voice interactions.
Architect and implement end-to-end generative AI pipelines—covering speech-to-text (ASR), LLM-based reasoning, retrieval-augmented generation (RAG), and text-to-speech (TTS).
Lead the development of context-aware, multimodal AI agents capable of real-time voice interaction, memory retention, and adaptive decision-making.
Enable intelligent agent orchestration across devices and environments.
Collaborate with cross-functional teams (product, design, data science) to define project scopes and deliverables.
Drive end-to-end project execution, from research exploration, cost and LLM model selection.