Interested in this AI Software Engineer role at Luxer One?
Apply Now →Skills & Technologies
About This Role
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.
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 e
Salary Context
This $145K-$180K range is below the median for AI Software Engineer roles in our dataset (median: $192K across 146 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At Luxer One, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($162K) sits 26% below the category median. Disclosed range: $145K to $180K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Luxer One AI Hiring
Luxer One has 1 open AI role right now. They're hiring across AI Software Engineer. Based in North Highlands, CA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.