About the Role
As an Applied AI Backend Engineer at Everfit, you'll work at the intersection of backend engineering and applied AI, building the scalable systems and AI-powered experiences that shape the future of fitness coaching for coaches and their clients around the world.
This is not a backend role with AI sprinkled on top. Expect roughly 70 percent of your work on LLM-powered systems (agent workflows, RAG, tool calling, evals) and 30 percent on the core backend services that power them.
Responsibilities
What you'll build
- Durable, long-running AI workflows on Temporal: scheduled jobs and multi-step agent processes that survive restarts, retries, and partial failures.
- Reliable AI pipelines with validation, retries, fallbacks, guardrails, and observability.
- Systems designed around AI-specific constraints: latency, token cost, model failures, quality.
- Production integrations with OpenAI and Anthropic models.
- The path from experiment to production: take a validated AI capability from the AI team and ship it as a service with clear reliability, cost, and quality characteristics.
- Evals and monitoring that define how AI features are measured, and catch regressions before users do.
How you'll work
- Scalable backend services in Node.js/TypeScript, with system architecture, technical design, and code review across Everfit's backend.
- Rapid experiments with emerging AI tech, turning the successful ones into production solutions.
- Day-to-day with Product Managers, AI Engineers, and QA, from technical discovery through delivery.
- Investigating production issues and improving AI workflows from real user feedback.
- Helping the broader engineering org adopt AI-native development practices.
Requirements
- 4+ years of professional backend engineering experience.
- Strong programming experience (preferably Node.js / TypeScript / Python) and backend architecture. Our core backend is Node.js/TypeScript and our AI services are Python/FastAPI; you'll work mainly in Node but should be comfortable crossing into Python when the work lives there.
- Solid understanding of REST APIs, microservices, distributed systems, databases, and scalable system design.
- Strong experience with databases such as MongoDB, PostgreSQL, and Redis.
- You have shipped at least one LLM-powered feature to production for real users, working directly with OpenAI or Anthropic APIs, and can talk concretely about its failure modes, evals, latency, and cost.
- You already use AI coding tools (Claude Code, Codex) daily, and you review what they produce with judgment about architecture, security, and edge cases.
- Comfortable experimenting with new AI tools and technologies and turning successful experiments into production solutions.
- Comfortable communicating in English and working with an international team.
- Curious, proactive, and excited about how AI can fundamentally change software development and product experiences.
Preferred Skills
- Experience with Python / FastAPI or AI-focused backend development.
- Experience building production LLM features, RAG pipelines, or agentic systems.
- Familiarity with the stack we run in production: Pydantic/LangGraph, Qdrant for vector search, Langfuse for LLM observability, and MCP.
- Experience with AWS, Docker, message queues, or cloud-native architecture.
- Experience improving engineering productivity through AI, with measurable impact.
- Experience working with health, fitness, wellness, personalization, or recommendation systems.
- Open-source contributions or personal projects involving AI.
Benefits
