Senior AI Engineer

About Aveni

Aveni is redefining how financial institutions use AI. We build real, production-ready agentic systems that reshape how banks, advisers, and financial services firms operate, replacing fragmented processes with intelligent, end-to-end workflows. We have been recognized as Fintech of the Year for two consecutive years and awarded Best Specialist Technology Service at the Professional Adviser Awards 2025.

Backed by leading investors including Puma Private Equity, and commercially supported by Lloyds Banking Group and Nationwide Building Society, we previously completed one of Scotland’s largest Series A rounds. Since then, we’ve continued to grow rapidly as adoption of AI in financial services accelerates, and we’re now scaling our team to meet that demand.

The Role

We’re looking for a Senior AI Engineer (FinLLM / NLP) to help drive the next generation of AI systems powering financial services. This role focuses on developing, fine-tuning, and deploying advanced language models that operate at production scale within highly regulated environments.

You’ll work alongside our NLP team, software engineers, and product leaders to build cutting-edge solutions that transform how financial organizations use AI. This is a hands-on role where you’ll shape both the technical direction and practical deployment of large language models and NLP systems within our platform.

What You’ll Do

• Build and optimize LLM-powered systems
• Architect, fine-tune, and optimize machine learning models including LLMs and classical ML approaches
• Experiment with modern architectures such as Transformers and Mixture-of-Experts
• Apply efficient training and fine-tuning techniques including LoRA and parameter-efficient methods
• Develop robust evaluation approaches to measure model quality and reliability
• Develop scalable AI systems
  • Build and deploy production-grade NLP pipelines and services
  • Ensure models operate efficiently with low latency and high availability
  • Integrate models into scalable cloud infrastructure and real-world applications
• Data strategy and model training
  • Lead the acquisition, processing, and governance of large structured and unstructured datasets
  • Apply exploratory data analysis and validation techniques to improve training pipelines
  • Ensure data privacy and governance standards suitable for financial services
• Engineering best practices
  • Contribute to DevOps and MLOps pipelines
  • Implement robust version control, testing, and CI/CD workflows
  • Support reliable deployment and monitoring of models in production environments
• Technical leadership
  • Mentor engineers and contribute to the technical growth of the team
  • Work closely with product, engineering, and data teams to deliver AI solutions
  • Stay at the forefront of AI and NLP research, applying new approaches where they add value