AI Engineer

Scottsdale, AZ Lodge Link is actively recruiting an AI Engineer to join our Scottsdale based team, and be part of the creation of the future of workforce travel. As an AI Engineer, you'll design, build, and operate the AI capabilities that power Lodge Link's customer and internal workflows: LLM-powered pipelines, retrieval systems, agents, and automations that run reliably in production.

A meaningful part of your work will involve designing and building AI agents and the tools that power them. This includes developing MCP servers that expose Lodge Link's internal capabilities to AI systems, integrating with real backend systems along the way, including REST APIs and a GraphQL-based legacy platform. Comfort working across different API patterns matters here, even if you haven't used all of them before.

The ideal candidate brings proven experience building and shipping LLM-powered systems to production—combining strong Go (and Python) engineering skills with hands‑on expertise in agents, retrieval, and API integrations. You can navigate REST and evolving architectures, debug end‑to‑end AI system performance, and apply sound judgment in model selection, cost management, and when to use (or not use) AI.

Our backend is built in Go, so you'll primarily be building AI integrations using Go SDKs and embedding capabilities into existing services. Python will come into play as well, particularly for AI tooling and scripting where the ecosystem is stronger.

This role is in Scottsdale and is in‑office with 20% flexibility to work from home.

• Contribute to MCP design and implementation: context management, tool routing, and orchestration patterns
• Build LLM-powered agents, RAG pipelines, and workflow automations connected to live business processes and APIs
• Integrate AI capabilities into existing systems via REST and GraphQL interfaces, learning what you don't already know along the way
• Participate in a disciplined models testing and selection and cost decisions, knowing when a smaller model or a rule-based approach does the job better

• Write production-quality Go: tested, versioned, observable, and deployable through standard CI/CD pipelines
• Instrument AI systems for cost, latency, and output quality; iterate based on what the data tells you
• Embed access controls, auditability, and safe‑use patterns into systems by default

• Work with product, data, engineering and Dev Ops teams to translate workflow requirements into AI system design
• Push back clearly when AI isn't the right tool for a given problem
• Document your systems so teammates can build on and maintain them

• Production Go development: services, APIs, testing, and deployment;
  Python proficiency for AI tooling, scripting, and ecosystem interoperability
• Hands‑on experience with LLMs: context engineering, embeddings, retrieval, and tool use
• Comfortable working with APIs; REST fluency is expected, GraphQL experience is a plus but not a requirement
• Ability to debug AI system behaviour across the full stack: retrieval quality, context management, cost, and output consistency
• Familiarity with CI/CD, observability tooling, and operational monitoring

• 3-5 years of software engineering experience, with Go as a primary language or demonstrable comfort working in it
• At least one LLM-powered system shipped to real users: agents, RAG pipelines, prompt orchestration, or similar
• Experience integrating with third‑party or legacy systems via APIs in a production environment
• Exposure to agent frameworks or orchestration tooling (Lang Graph, Llama Index, or equivalent) is an asset
• Background in compliance-sensitive or audit‑driven environments is an asset

• Curious and growth‑oriented: you're energized by working in a fast‑moving space where not everything is figured out yet
• Pragmatic: you reach for the simplest solution that works and resist over‑engineering
• Accountable: you own what you ship, including production issues
• Collaborative: you work well across product, data, and Dev Ops without needing a formal handoff process
• Clear communicator: you can explain AI system…