Backend Engineer (AI Platform)

As a Backend Engineer you will build the services our products and client solutions run on. The center of gravity is LLM infrastructure: the layer that puts one governed front door over many models, hosted and self-hosted, and answers the questions enterprises actually ask - who used which model, for what, at what cost, under whose rules. The platform’s first customers are our own client-facing engineers, the people delivering client outcomes with what you build, so requirements arrive concrete, feedback arrives same-day, and the people you support are in the same meeting., * Own the LLM gateway: routing, metering, budgets, guardrail composition, and provider/backend adapters across hosted and self-hosted models.

• Manage the retrieval and knowledge service: connectors, chunking, hybrid search and reranking, grounded answers, and the MCP surface.
• Build the eval harness that turns retrieval tuning from judgment into evidence, with confidence intervals and per-segment breakdowns.
• Own the async framework - its public API, rate-limit algorithms, and admin tooling - and lead its evolution into durable execution for agent runs.
• Design authorization across the whole surface: token hierarchies, capability grammars, and per-tenant isolation tested as a regression, not asserted in a doc.
• Own your own definition of done on everything you ship: evals, traces, cost accounting, and the dashboard that would wake you up.

• 5+ years of deep, production async Python - cancellation scopes, streaming lifecycle, and connection pooling
• Postgres as infrastructure - comfortable reasoning about MVCC, advisory locks, and vacuum discipline, with Redis/Dragonfly used (and not used) where it belongs; real distributed-systems experience, not just familiarity.
• Experience shipping API surfaces other engineers build on - versioning, idempotency, error vocabularies, migration discipline, and documentation to match.
• Depth in at least one of LLM gateway concern (routing, metering, guardrails, provider failover) or retrieval engineering (hybrid search, reranking, eval methodology, audit-ready RAG), with credibility on the other.
• A cost accounting and evals first mindset - you’ve shipped a gate or harness that caught a real regression, ideally one of your own.
• Production depth in some modern stack, and the aptitude to ramp quickly on unfamiliar tools
• Practical familiarity with our core stack - Python 3.12+ (async, type-strict, FastAPI/Starlette, Pydantic v2), asyncpg/SQLAlchemy, Postgres (incl. pgvector), and Redis/Dragonfly with Lua - with willingness to research into the rest.
• Exposure to the surrounding ecosystem: OpenTelemetry (incl. GenAI conventions), SSE/streaming lifecycles, OpenAI/Anthropic provider APIs, job frameworks (Celery/Dramatiq/Prefect-class), rate-limit algorithms (token bucket, GCRA), and integer-cents money handling.
• Past work in energy, real estate, utilities, climate or related fields is a plus
• Experience in both startup and enterprise environments is a plus

• Competitive early-stage startup compensation (based on capabilities, experience, and location)
• Bonus eligibility
• Health insurance with meaningful coverage for dependents
• Flexible paid time off
• Equity
• Fully remote culture with a cluster of teammates in Seattle

About AZX

Our mission is to accelerate positive impact in critical industries through AI transformation. We specialize in physics-informed ML and enterprise AI solutions that directly address climate and sustainability challenges.

We’re growing quickly and already work with category-leaders in real estate (CBRE), energy (LevelTen Energy), logistics (Flexe) and utilities.

We’re a public benefit corporation, founded in 2024, and have been profitable from inception.

We work on challenges in clean energy, decarbonization, climate risk, energy systems, and global economics. We’re building our company for long-term success and aim to create the ultimate place to work for those passionate about AI and making a positive impact.