Artificial Intelligence Engineer

Roles and Responsibilities :

Key Responsibilities

1. Model training and fine-tuning (top priority): Train, fine-tune, and improve encoder and decoder models for our metrics. Own the full loop from data to evaluated, production-ready checkpoints.
2. Dataset and metric development: Design, build, and curate datasets for new and existing metrics. Define labelling schemes, manage data quality, and connect dataset changes to measurable model improvements.
3. Experimentation and evaluation: Run systematic experiments across model architectures, batch sizes, precision, and training hyperparameters. Build and maintain rigorous evaluation harnesses and track results to find the best accuracy/cost trade-offs.
4. Inference optimisation: Optimise trained models for production inference on the Nvidia stack (Triton, TensorRT / TensorRT-LLM, ONNX) using methods such as quantization, distillation, and precision tuning.
5. Model deployment pipeline: Maintain and improve the pipeline (Docker-based) that converts models to suitable formats (TensorRT, ONNX), fixes vulnerabilities, and containerises them for production.

Job Requirements :

Required Skills

Strong practical experience training and fine-tuning deep learning models, with a proven track record of taking models from data to production

PyTorch, and a solid understanding of the inner workings of Transformers and deep learning more broadly

Dataset construction and model evaluation: building datasets, defining metrics, and designing evaluation methodology

Python

Nvidia Inference Stack: Triton Inference Server, TensorRT / TensorRT-LLM, ONNX

Docker and Kubernetes (k9s familiarity is a plus)