AI/ML Engineer

About the Role:
Our model layer is the product, not a feature of it. This role builds and maintains the pipelines behind it - fine-tuning runs, retrieval components, evaluation datasets and the scoring harness that decides whether a model version ships.
You work under the AI/ML Lead alongside our AI fellows, which means part of the job is setting the standard for how experiments are run and recorded. This is applied engineering, not research: the measure is whether it works in production for a client.
What You Bring:
3–5 years in applied machine learning, or an AI fellowship with demonstrated production contribution.
Strong Python; PyTorch and the Hugging Face ecosystem as daily tools.
You have run a fine-tune and can explain why it did or did not move the metric.
Retrieval pipelines built and tuned in production — and measured, not assumed.
You write evaluation datasets rather than judging model output by eye.
You can read a paper and tell whether it is worth implementing.
What Would Be Great to Have:
OCR and document extraction pipelines.
Speech and audio processing.
Quantization and inference optimization.
Model governance and documentation under ISO 42001.
Tech Stack:
ML: Python, PyTorch, Hugging Face Transformers, PEFT (LoRA / QLoRA), scikitlearn
Retrieval: Embedding models, vector search, reranking, chunking strategies
Evaluation: Golden datasets, regression suites, RAGAS or equivalent
Serving: vLLM or TGI, quantisation, batching, latency management
Platform: FastAPI, PostgreSQL, Docker, AWS

Work arrangement
No