Senior AI Engineer

Senior AI Engineers at Treasure AI lead the development of sophisticated AI features — owning LLM integrations, agentic workflows, and AI product experiences from design through production. You own the AI product layer — LLM integration, prompt design, and evaluation — working with Software Engineers to build the features that consume and deliver it. At this level you don't just execute within established patterns — you define them for your pod (a 3–4 person delivery team) and elevate everyone around you. Success means shipping AI features that work reliably at scale, setting the LLM integration standard for your pod, and staying on the leading edge of what's possible with language models.

Lead the design and implementation of AI-powered features: LLM integrations, retrieval-augmented generation, agentic pipelines, and AI-augmented workflows

Own prompt engineering rigor — structure prompts systematically, evaluate outputs, iterate based on production signal, and document what works

Develop LLM integration patterns for the pod: streaming responses, function calling, context window management, fallback handling, and evaluation

Understand the AI solution space well enough to recommend the right approach — knowing when to reach for prompting, RAG, fine-tuning, or agentic patterns, and when to bring in additional expertise

Contribute production-grade code in Ruby/Rails and TypeScript/React; hold a high bar for code quality in AI and non-AI features alike

Use Claude Code and GitHub Copilot fluently; critically review AI-generated code and help teammates do the same

Work with our customer-facing agent platform to build, iterate, and deploy AI capabilities

Independently scope AI feature work within the team's 3-week delivery cadence, accounting for experimentation cycles and evaluation needs

Own on-call shifts; develop operational instincts for AI systems in production — including latency, cost at scale, multi-tenant routing, and customer data boundaries

Ship AI features iteratively — deliver a working version early and refine based on real usage

Conduct deep code reviews on AI feature work; help teammates reason about model behavior, prompt design, and integration robustness

Share LLM integration knowledge across the pod and contribute to cross-pod AI engineering discussions

Help peers grow their AI engineering skills through pairing, documentation, and structured feedback

Champion shared ownership of AI systems — avoid knowledge silos around model behavior and integration details

Potential need for infrequent travel to Mountain View, California. Usually one week or less a year.