Live opening · Posted 7 days ago
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Description supplied by the original job listing.
About the company
SKXYWTF builds Exponential Yield (XY) financial products with a social and sustainable mandate — maximising returns while advancing market equity. We sit at the intersection of quantitative finance and applied AI.
The role
You'll join a small, fast-moving AI engineering team working on next-generation language model applications and agentic systems. This is hands-on product engineering — not research busywork — with direct exposure to production AI infrastructure and real users.
What you'll work on
Build and ship features for LLM-powered products, including RAG pipelines, structured output layers, and tool-calling agents
Design and integrate agentic workflows using orchestration frameworks (LangGraph, LlamaIndex, or similar)
Work with model evaluation frameworks — write evals, run benchmarks, and iterate on prompt engineering
Build and maintain observability tooling: tracing, logging, and latency monitoring for AI inference in production
Contribute to model context management — chunking strategies, embedding pipelines, vector store integration
Participate in code reviews, architecture discussions, and documentation
What we're looking for
Currently pursuing a BS or MS in Computer Science, Engineering, or a related field
Solid Python fundamentals; experience with TypeScript, Go, or Rust is a strong plus
Familiarity with LLM APIs (OpenAI, Anthropic, Gemini, or open-source equivalents via Ollama/vLLM)
Exposure to RAG patterns, vector databases (Pinecone, Weaviate, pgvector), or embedding workflows
Working knowledge of containerisation (Docker) and comfort with cloud environments (AWS / GCP / Azure)
Understanding of agentic patterns: tool use, function calling, ReAct loops, multi-agent coordination
Familiarity with model evaluation concepts — LLM-as-judge, benchmark design, RAGAS or similar
Excellent communicator; can write clearly and discuss tradeoffs in technical reviews
Curiosity-driven — you read release notes, follow model launches, and have opinions about context windows
Core stack
Python
REST / async APIs
Docker
Git
SQL
AI / ML layer
LLM APIs
RAG pipelines
Vector DBs
Prompt engineering
LangChain / LangGraph
LLM evals
Structured outputs
Agentic workflows
Nice to have
TypeScript
React / Next.js
Kubernetes
OpenTelemetry / tracing
Fine-tuning (LoRA / QLoRA)
MCP servers
Solidity / Web3
ℹ
Unpaid internship · 3 months. This is a pre-revenue startup engagement. Interns gain direct production experience, mentorship, and a verifiable portfolio of shipped AI systems.
Work arrangement
Yes
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