ML Engineer / AI Platform Lead

BTSE

1d

About The Position

You own the AI core: model serving, the retrieval-augmented generation (RAG) pipeline, prompt engineering, and the feedback-to-training pipeline. In Phase 1, you make the base model perform as well as possible through context engineering — system prompts, few-shot exemplars, and retrieval optimisation — without modifying model weights. You also design the custom model training workflow so that enterprise clients can train their own fine-tuned models in Phase 2. This is the highest-leverage individual contributor role on the founding team.

Requirements

• 5+ years ML engineering; 2+ years working with large language models in production.
• Hands-on experience with LLM serving frameworks (vLLM, TGI, or equivalent).
• Deep experience building RAG pipelines: chunking strategies, embedding models, vector databases, reranking.
• Strong prompt engineering skills for production applications — you know how to make a base model produce consistent, structured, high-quality output.
• Python: PyTorch, Transformers, FastAPI.
• Familiar with LoRA/QLoRA fine-tuning workflows.

Nice To Haves

• Experience building multi-tenant ML serving infrastructure.
• Experience with financial or crypto AI applications.
• Experience with cross-encoder reranking models (DeBERTa or similar).
• Understanding of data isolation requirements for ML training pipelines.

Responsibilities

• Deploy and optimise a large language model for production inference: quantisation, continuous batching, low-latency serving.
• Build the RAG pipeline: document chunking, embedding generation, vector storage, cross-encoder reranking, and context assembly optimised for a 128K-token context window.
• Build the context layer: per-tenant system prompts, dynamically retrieved few-shot exemplars, task routing (classifying incoming requests to the right prompt configuration).
• Build defensive output parsing: structured JSON output from an unmodified base model with graceful fallbacks.
• Design and implement the feedback collection pipeline: capturing user corrections and ratings, automatically generating training data candidates for future fine-tuning.
• Design the custom model training workflow: tenant-scoped LoRA training on client-specific data, model evaluation, A/B testing, and isolated deployment.
• Monitor and improve inference quality: parsing failure rates, citation accuracy, hallucination rates, latency — all tracked per tenant.
• Iterate on prompts daily with the domain expert during the pilot phase.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 1-10 employees