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# Staff Software Engineer - AI

- **Company:** Luxer One
- **Location:** North Highlands, CA, United States
- **Salary:** $145,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Business Software, Microsoft Outlook, Data Integration, Software Debugging, Systems Integration, Zoho Office Suite, Facebook Ads Manager, Enterprise Software Applications, Retrieval-Augmented Generation, Multi-Agent Systems, Prompt Engineering, Backend, Kubernetes, Slack, Invoking Functions, Model Context Protocol
- **Published:** October 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=34e620088b86ec3a

## About the Role

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., Demonstrated experience designing and shipping production agentic AI systems - not prototypes, not fine-tuning exercises, not proof-of-concept demos
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Strong software engineering fundamentals - this role builds real systems, not wrappers; you write code, design integrations, and own what you deploy
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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
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Comfort operating with high autonomy and ambiguity - staff-level judgment about what to build and how, given a business problem and a desired outcome
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Demonstrated ability to work directly alongside non-technical stakeholders - translating business problems into technical architectures independently, without a product layer in between
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Integration experience with business application APIs: CRM, email and marketing platforms, calendar systems, communication tools, advertising APIs, or similar
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Experience designing and running evaluation suites for agentic systems - you do not ship agents without a baseline and a monitoring strategy
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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

## Description

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
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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
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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
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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
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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
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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
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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:
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Deep, hands-on experience building agentic AI systems - multi-step reasoning, tool use and function calling, orchestration, multi-agent workflows, retrieval, memory, and evaluation
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A track record of shipping AI systems against real business metrics, not just technical benchmarks
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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