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About This Role
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
The Supply Chain AI & Data team drives the strategic transformation of our operations in alignment with Vision 2030. We are building the next evolution of data on AI—moving beyond traditional analytics to architect an intelligent, autonomous supply chain ecosystem. As the strategic engine behind critical initiatives like Project Falcon, our mission is to fundamentally redefine how the business operates, turning complex logistical challenges into proactive, resilient, and forward-looking capabilities. Job Description
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.
Key Responsibilities
- 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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
#LI-MM4
#LI-HYBRID
Basic Requirements
- 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
Travel Requirements
Under 10% Relocation Provided
None Position Type
Experienced Referral Payment Plan
Yes Our U.S. Benefits include:
- Incentive Bonus Plans
- Medical, Dental, Vision benefits
- 401K with Company Match
- 10 Paid Holidays
- Generous Paid Time Off Packages
- Employee Stock Purchase Plan
- Paid Parental & Family Leave
- and more!
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.
We’re committed to providing an inclusive and accessible recruiting experience for candidates with disabilities, or other physical or mental health conditions. To request an accommodation, please complete this Reasonable Accommodations Form so we can assist you.
Salary Context
This $75K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Motorola Solutions, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $75K to $125K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Motorola Solutions AI Hiring
Motorola Solutions has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Waltham, MA, US, Chicago, IL, US, Waltham, MA. Compensation range: $125K - $220K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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