Description

Lead applied research, fine-tuning, and deployment of Large Language Models for production environments.

This role is on-site.

Responsibilities

• Lead applied research into open-source LLM evaluation, cost optimization, and data-residency strategies.
• Run fine-tuning work for tone, lexicon, and domain-specific tasks currently brittle in prompts.
• Investigate inference efficiency, tokenomics, cost-per-output, and production inference optimization.
• Prototype voice and multimodal capabilities including speech-to-text, text-to-speech, and document understanding.
• Partner with platform engineers to productionise research outputs tied to real product needs.

Required Skills

• 5+ years of applied experience in artificial intelligence and machine learning.
• Hands-on experience with evaluating, fine-tuning, and deploying Large Language Models.
• Proficiency in Python, PyTorch, and Hugging Face.
• Deep understanding of Machine Learning and Deep Learning principles.
• Experience with experimental design and rigorous evaluation methodologies.
• Published research in top-tier AI/ML venues or equivalent open-source contributions.
• MSc or PhD in Machine Learning, Computer Science, or related quantitative field.

Preferred Skills

• Experience with speech, voice models, and audio ML.
• Familiarity with data-residency, sovereignty considerations, and Responsible AI in regulated environments.
• Background in financial services, particularly wealth management or asset management.

Key Skills

Education

Any Graduate