Lead AI Integration Strategist (Cloud & Communications)

Conduct rigorous risk assessments (security, bias, latency, hallucination risks) to ensure proposed AI solutions are safe for mission-critical communication environments.

Develop proof-of-concepts (PoCs) to validate the performance, cost-to-serve, and operational impact of new AI tools before wide-scale rollout.

2. Operational & Process Optimization

Identify bottlenecks in development, QA, and deployment pipelines, deploying AI tools (e.g., AI-assisted code reviews, automated test generation) to enhance engineering productivity.

Implement AI-driven telemetry and predictive maintenance for cloud infrastructure to proactively detect outages and reduce compute costs.

Streamline customer support and onboarding processes using secure, RAG-based (Retrieval-Augmented Generation) knowledge assistants for dispatchers and IT admins.

3. Product Enhancement (Communications in & Cloud Solutions)

Audio & Speech AI: Propose and integrate edge-to-cloud audio models. Focus on advanced audio features, i.e. noise suppressors, real-time speech-to-text in high-noise environments, and acoustic anomaly detection.

Dispatch & Cloud Intelligence: Design AI integrations for cloud dispatch systems, such as automated radio chatter summarization, predictive location-based resource allocation, and smart keyword alerting.

Performance Optimization: Leverage predictive AI to optimize cloud routing, reduce latency in PTT call setup, and manage bandwidth dynamically over LTE/Wi-Fi networks.

Basic Requirements

Experience & Mindset

Experience: 5+ years in AI/ML engineering, product management, or technical strategy, ideally within cloud SaaS, telecommunications, or unified communications (UCaaS).

Risk-Aware Innovation: Demonstrated ability to distinguish between "hype" and "stable, production-ready" AI. Experience deploying AI in zero-fault tolerance or high-reliability environments.

Cross-Functional Leadership: Proven track record of translating complex AI concepts into business value (ROI, cost-savings, performance metrics) for stakeholders.

Technical Competencies

Audio/Voice ML: Familiarity with audio processing models, speech recognition (ASR), and natural language processing (NLP) tailored for real-time communication.

Cloud AI Services: Deep understanding of managed AI services across major cloud providers (AWS, Azure, GCP) and how to architect them for cost-efficiency.

Edge vs. Cloud Architecture: Strong understanding of when to process AI workloads on the edge (e.g., on rugged PTT devices) versus in the cloud to balance latency, battery life, and compute costs.

MLOps: Knowledge of model deployment, monitoring, and lifecycle management to ensure AI features do not degrade over time.

Travel Requirements

None

Relocation Provided

None

Position Type

Experienced

Referral Payment Plan

Yes

EEO Statement

Motorola Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion or belief, sex, sexual orientation, gender identity, national origin, disability, veteran status or any other legally-protected characteristic.

We are proud of our people-first and community-focused culture, empowering every Motorolan to be their most authentic self and to do their best work to deliver on the promise of a safer world. If you’d like to join our team but feel that you don’t quite meet all of the preferred skills, we’d still love to hear why you think you’d be a great addition to our team.