Own our fine-tuning strategy end-to-end — LoRA first, full fine-tunes where they earn it. What we tune, on what hardware, against what evals
Define the GPU architecture. We have working infrastructure (H100 for production, A100 for training); your call to confirm, reshape, or rebuild
Drive model selection and routing across Gemini, Anthropic, and OpenAI — the right model for the right job, with cost and latency in the equation
Build agentic LLM pipelines using LangChain, LangGraph, and LangSmith
Design and iterate on prompt strategies, with a focus on consistency and context
Construct retrieval-augmented generation (RAG) systems from scratch
Instrument evaluation metrics, telemetry, and feedback loops to guide model and prompt evolution
Work alongside product, frontend, and backend engineers to tightly integrate AI into user-facing flows

8+ years building production-grade ML, data, or AI systems
Hands-on experience training and fine-tuning models — LoRA, QLoRA, adapter methods, or full fine-tunes.
Actual model work, not just prompt iteration
Confidence to define GPU architecture given a goal and a budget — hardware choices, training strategy, cost/performance tradeoffs
Strong grasp of prompt engineering, context construction, and retrieval design
Comfortable working in LangChain and building agents, not just chains
Strong Python: testable, maintainable, clearly structured
Understanding of model evaluation, observability, and feedback loops
Excited to push from prototype → production → iteration
Senior IC judgment: you scope your own work, push back when it's right, and make calls others can build on
Confident English skills to collaborate clearly and effectively with teammates.

HyperFi

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