ML Engineer / AI Platform Lead

Hong KongRemoteFull-timelead

Data ScienceOtherMachine Learning EngineerData

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Overview

About BTSE: BTSE Group is a global leader in fintech and blockchain technology, anchored by three core business pillars: Exchange, Payments, and Infrastructure Development.

Technical Tools

Data ScienceOtherMachine Learning EngineerData

About BTSE:

BTSE Group is a global leader in fintech and blockchain technology, anchored by three core business pillars: Exchange, Payments, and Infrastructure Development. Serving over 100 corporate clients worldwide, we provide white-label exchange and payment solutions. Our offerings encompass everything from exchange infrastructure hosting and development to custody, wallets, payments, blockchain integration, trading, and more. We are looking for talented professionals in marketing, operations, customer support, and other departments. The roles offered may be on-site, remote, or hybrid, in collaboration with our local partner.

About the opportunity:

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.

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.

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.

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.