Junior Full-Stack Developer

About the Role

We are looking for a Junior Full-Stack Engineer to help build and ship product features that put generative AI directly in users' hands. You will work across the stack — frontend chat and assistant interfaces, backend services that orchestrate LLM calls, and the data plumbing around them — collaborating closely with senior engineers, designers, and product to turn AI capabilities into reliable, user-facing functionality. This is a hands-on role for someone early in their career who is genuinely curious about LLM systems and wants to build real production experience with them.

Responsibilities

• Build and maintain user-facing AI features such as chat interfaces, document Q&A, summarization tools, and inline assistants.
• Develop frontend components that handle streaming LLM responses, loading states, retries, and graceful error handling.
• Implement backend endpoints and services that orchestrate LLM calls, manage conversation context, and apply business logic around model outputs.
• Write and refine prompts, system instructions, and tool/function definitions for production use cases.
• Integrate embeddings and vector retrieval (e.g., pgvector, Pinecone, Weaviate) for retrieval-augmented generation (RAG) over internal content.
• Add logging, tracing, and basic evaluation hooks so the team can monitor quality, latency, and cost.
• Write tests — including for non-deterministic AI outputs — and participate in code review.
• Contribute to technical decisions as you grow, and help document patterns the rest of the team can reuse.

Requirements

• Practical experience (internships, bootcamp projects, contributions, or 1+ year professional) building web applications end-to-end.
• Solid foundation in at least one modern frontend framework (React, Next.js, Vue, or Svelte) including component composition, state management, and async data fetching.
• Backend experience in Node.js, Python, or Go: building REST or GraphQL APIs, handling I/O, working with JSON.
• Hands-on use of at least one LLM API (OpenAI, Anthropic, Google, or open-source via Ollama/vLLM/Hugging Face).
• Understanding of core GenAI concepts: prompts vs. system prompts, context windows, tokens, temperature, and the difference between chat and completion APIs.
• Comfort with Git, branching workflows, and basic CI/CD.
• Strong written communication; ability to explain technical trade-offs clearly.

Nice to Have

• Experience with streaming responses (Server-Sent Events, WebSockets, fetch streams).
• Familiarity with vector databases, embeddings, and retrieval pipelines.
• TypeScript across the stack.
• Experience deploying to a cloud platform (AWS, GCP, Vercel, Fly.io, etc.).
• Observability tooling (LangSmith, Langfuse, OpenTelemetry, Datadog).
• Side projects, open-source contributions, or writing about AI you have shipped or studied.