Full Stack Engineer - AI/LLM Applications

Job descriptionAbout the RoleAt BeeHyv, we build AI-powered products and agent systems for enterprise clients across healthcare, insurance, and government. This is a hands-on engineering role for someone who already builds full stack web applications and has started shipping real features on top of LLMs not just demos.You will work close to the problem: turning client requirements into working software, deciding how (and whether) to apply an LLM, and shipping reliable, observable, maintainable systems. You will pair with senior engineers and forward-deployed teams, so you will grow quickly on both the product and AI sides.What You'll Work On- Build and ship full stack features APIs, data models, and front-end for client-facing AI products.- Implement RAG pipelines over enterprise documents and databases: ingestion, chunking, embeddings, and retrieval.- Build agentic workflows and tool-calling features using Claude and orchestration frameworks such as LangGraph.- Add the unglamorous-but-essential parts: guardrails, evals, logging, tracing, fallbacks, and cost/latency monitoring.- Integrate with client systems and third-party APIs,and help take features from prototype to production.Core Stack- Backend: Python (FastAPI) or Java (Spring Boot) strong depth in one. Solid REST API design; comfort with async work, background jobs, and queues.- Frontend: A modern framework React, Angular, or Vue with TypeScript. Clean, responsive UI; sensible component structure and state management.- Data: PostgreSQL (including pgvector) and Redis. Schema design and query tuning.- AI layer: Anthropic / Claude APIs, embeddings and vector search, plus an orchestration framework (LangGraph or LangChain).- Cloud & DevOps: AWS or Azure, Docker, and a working understanding of CI/CD and deployment.What We're Looking ForRequired- 3-5 years building and shipping production web applications, end to end.- Strong in one backend Python or Java/Spring Boot and one modern front-end framework (React, Angular, or Vue with TypeScript).- 12 years of hands-on LLM application work you've built at least one real RAG or tool calling feature that ran in front of users.- Working knowledge of prompt design, embeddings, vector search, and chunking trade offs.- Hands-on multi-agent orchestration you've built agentic/tool-calling workflows with a framework such as LangGraph, CrewAI, AutoGen, or Semantic Kernel.- Practical AI observability tracing, evals, and monitoring of LLM features (e.g., Langfuse, Grafana, or equivalent), including cost and latency.- Comfort with SQL databases (Postgres preferred) and basic cloud deployment (AWS or Azure).- Clear written and verbal communication you can explain trade-offs to engineers and to clients.Nice to Have- Cloud AI services: AWS Bedrock, Azure AI Foundry, or Vertex AI.- Experience in regulated domains healthcare, insurance, or government.- Node.js, alternative vector stores (Pinecone, Weaviate, Qdrant, Chroma, FAISS), or local/open-source model deployment.How We EvaluateOur process is designed to respect your time:1. Intro conversation your background and what you've built.2. Technical discussion a walk-through of a real AI feature you've shipped, plus a focused full stack + LLM exercise.3. System & design conversation how you'd design a RAG or agent feature, including guardrails and trade-offs.4. Final conversation with the engineering lead. (ref:hirist.tech)