Job description

About Luxer One Luxer One is the leader in smart locker and package management solutions across multifamily, commercial, retail, and enterprise markets. We design, manufacture, and deploy intelligent locker systems that move packages, assets, and people more efficiently — serving millions of transactions per year across North America. As the Assa Abloy Secure Lockers Division, we combine the resources of a global leader with the speed and focus of a product-driven technology company. We are building the AI infrastructure that will define how this business operates over the next decade — not as an experiment, but as the production backbone of our sales, operations, customer success, and field teams. Why This Role Exists Most AI initiatives inside companies fail the same way: compelling demos, no production systems. The gap is not the AI. It is the last mile — getting powerful models into the actual workflows that run the business. That is what this role is built to solve. Luxer One is embedding Forward Deployed Agentic AI Engineers directly inside business units across the organization. These engineers do not sit in a central platform team waiting for tickets. They embed with GMs, sales leaders, operations teams, and customer success functions. They learn the work, find the highest-leverage opportunities, and ship agentic AI systems that change how the business operates — measured by outcomes, not activity. This is the fastest-growing engineering archetype in the industry. Palantir built a category-defining company around it. OpenAI, Anthropic, Google, AWS, and Microsoft are each building internal versions at scale. We are doing it from the inside, deploying this model across our own divisions. If you have ever looked at a broken sales workflow, a high-volume support queue, or a manual operations process and thought "I could build an AI system that eliminates this" — and then actually built it — read on. Who You Are You are an engineer who is equally at home debugging an integration and presenting ROI to a GM. You move fast, operate independently, and measure your work by what changed in the business — not by lines of code shipped. Specifically, you are: • An engineer first — you design and ship complete production systems from scratch, across the full stack: data integration, agent orchestration, prompt and context engineering, evaluation, deployment, and monitoring • Business-fluent — you can walk into a sales standup, a field operations review, or a GM strategy session and come out with a clear list of the highest-ROI AI opportunities in the room, without anyone handing you a brief • Self-directed — you do not need a spec, a PM, or a fully defined sprint to get started; you get handed a business outcome and you figure out how to build the system that delivers it • Adaptive — every business unit runs a different stack; you learn the tools, find the integration points, and build on what exists — whether that is Zoho CRM, Microsoft Outlook, Slack, Meta Ads Manager, or something you have never touched before • Outcome-obsessed — you define what success looks like before you build, and you measure it in production; ROI on your agentic systems is your scorecard, not technical throughput • An effective communicator across roles — you can explain what you are building to a GM, a frontline sales rep, and a backend engineer using three completely different vocabularies, all in the same week • Production-minded — your systems run in production, monitored, evaluated, and maintained; prototypes and demos are just the first five minutes of the work And you bring: • Deep, hands-on experience building agentic AI systems — multi-step reasoning, tool use and function calling, orchestration, multi-agent workflows, retrieval, memory, and evaluation • A track record of shipping AI systems against real business metrics, not just technical benchmarks • Integration experience across business application APIs — CRM platforms, email and marketing tools, calendar systems, communication tools, advertising platforms, or similar What You'll Do • Embed with an assigned business unit and participate in their operational cadences — standups, pipeline reviews, field debriefs, ops meetings — as an active contributor, not an observer • Identify, scope, and prioritize agentic AI opportunities proactively and independently — without waiting for a product manager to hand you a project • Build and ship complete production systems end to end: from business problem definition through integration, orchestration, prompt engineering, evaluation design, deployment, and ongoing monitoring • Own the ROI of every agentic workflow you deploy — define what success looks like, instrument it in production, and report results clearly to the business unit and the AI Engineering Lead • Work directly with GMs, functional leaders, frontline teams, and operators — developing enough context about their actual work to identify AI leverage points they have not yet articulated • Integrate across the full technology stack of the assigned business unit — CRM, email and marketing platforms, scheduling systems, ERP, field management tools, advertising APIs, and others as required • Manage your deployed systems in production — monitor behavior, run regression evals, identify drift, and improve continuously as the business evolves • Extract reusable integration patterns, orchestration templates, and evaluation frameworks from your unit-specific work and contribute them back to the shared AI engineering library • Stay current on the rapidly evolving agentic AI landscape — new orchestration approaches, model capabilities, and framework patterns — and bring proven advances into production when they raise the ceiling on what is achievable Requirements What We're Looking For • Demonstrated experience designing and shipping production agentic AI systems — not prototypes, not fine-tuning exercises, not proof-of-concept demos • Strong software engineering fundamentals — this role builds real systems, not wrappers; you write code, design integrations, and own what you deploy • Hands-on depth across the agentic AI stack: orchestration frameworks, tool use and function calling, retrieval-augmented generation, multi-agent design, context engineering, and evaluation • Comfort operating with high autonomy and ambiguity — staff-level judgment about what to build and how, given a business problem and a desired outcome • Demonstrated ability to work directly alongside non-technical stakeholders — translating business problems into technical architectures independently, without a product layer in between • Integration experience with business application APIs: CRM, email and marketing platforms, calendar systems, communication tools, advertising APIs, or similar • Experience designing and running evaluation suites for agentic systems — you do not ship agents without a baseline and a monitoring strategy • Strong communication — you document what you build, explain how it works, and surface what is not working Strongly Preferred • Prior experience as a forward-deployed engineer, embedded technical consultant, or solutions engineer in an AI or enterprise software context • Experience building multi-agent systems — orchestrator/worker patterns, agent-to-agent coordination, shared state management • Familiarity with MCP (Model Context Protocol) or similar agent-to-tool integration standards • Background building agentic systems for sales, customer operations, field services, or business development workflows • Experience operating within small, high-autonomy teams where you owned the engineering direction on a project end to end • Track record of shipping AI systems that changed operational or financial metrics — with the numbers to show for it Benefits Location & Work Model • Sacramento, CA. On-site or hybrid Benefits Luxer Has Got You Covered! • You will have a 401k with up to 4.5% matching, untracked vacation, and a hybrid work schedule. • We promote education through tuition reimbursement. • You will also have medical, dental, vision, and life insurance programs, as well as employee assistance programs. • You’ll have opportunities to advance. • We’re fans of helping our employees learn different aspects of the business, be challenged with new tasks, be mentored and grow. • We promoted 42% of our employees last year! Luxer One is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other legally protected status. Compensation The pay range for this role is $145,000-180,000 per year, depending on experience, skills, and location. This range reflects what we believe in good faith we'd pay for this position at the time of posting, and final offers may vary based on qualifications and business needs .

Requirements

Must-haves

You are an engineer who is equally at home debugging an integration and presenting ROI to a GM. You move fast, operate independently, and measure your work by what changed in the business — not by lines of code shipped.
An engineer first — you design and ship complete production systems from scratch, across the full stack: data integration, agent orchestration, prompt and context engineering, evaluation, deployment, and monitoring
Business-fluent — you can walk into a sales standup, a field operations review, or a GM strategy session and come out with a clear list of the highest-ROI AI opportunities in the room, without anyone handing you a brief
Self-directed — you do not need a spec, a PM, or a fully defined sprint to get started; you get handed a business outcome and you figure out how to build the system that delivers it
Outcome-obsessed — you define what success looks like before you build, and you measure it in production; ROI on your agentic systems is your scorecard, not technical throughput
An effective communicator across roles — you can explain what you are building to a GM, a frontline sales rep, and a backend engineer using three completely different vocabularies, all in the same week
Production-minded — your systems run in production, monitored, evaluated, and maintained; prototypes and demos are just the first five minutes of the work
Deep, hands-on experience building agentic AI systems — multi-step reasoning, tool use and function calling, orchestration, multi-agent workflows, retrieval, memory, and evaluation
A track record of shipping AI systems against real business metrics, not just technical benchmarks
Integration experience across business application APIs — CRM platforms, email and marketing tools, calendar systems, communication tools, advertising platforms, or similar
Embed with an assigned business unit and participate in their operational cadences — standups, pipeline reviews, field debriefs, ops meetings — as an active contributor, not an observer
Identify, scope, and prioritize agentic AI opportunities proactively and independently — without waiting for a product manager to hand you a project
Build and ship complete production systems end to end: from business problem definition through integration, orchestration, prompt engineering, evaluation design, deployment, and ongoing monitoring
Own the ROI of every agentic workflow you deploy — define what success looks like, instrument it in production, and report results clearly to the business unit and the AI Engineering Lead
Work directly with GMs, functional leaders, frontline teams, and operators — developing enough context about their actual work to identify AI leverage points they have not yet articulated
Integrate across the full technology stack of the assigned business unit — CRM, email and marketing platforms, scheduling systems, ERP, field management tools, advertising APIs, and others as required
Manage your deployed systems in production — monitor behavior, run regression evals, identify drift, and improve continuously as the business evolves
Extract reusable integration patterns, orchestration templates, and evaluation frameworks from your unit-specific work and contribute them back to the shared AI engineering library
Demonstrated experience designing and shipping production agentic AI systems — not prototypes, not fine-tuning exercises, not proof-of-concept demos
Strong software engineering fundamentals — this role builds real systems, not wrappers; you write code, design integrations, and own what you deploy
Hands-on depth across the agentic AI stack: orchestration frameworks, tool use and function calling, retrieval-augmented generation, multi-agent design, context engineering, and evaluation
Comfort operating with high autonomy and ambiguity — staff-level judgment about what to build and how, given a business problem and a desired outcome
Demonstrated ability to work directly alongside non-technical stakeholders — translating business problems into technical architectures independently, without a product layer in between

Nice-to-haves

Strongly Preferred

Application prep

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aiaws

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Benefits

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Dental

Vision

401k