Gravitas has partnered with a well funded FinTech Start-Up specialising in building Gen AI financial advisory solutions for enterprise businesses.
As the Lead Machine Learning Engineer specialising in Generative AI, you will be at the helm of cutting-edge AI projects that will fundamentally reshape how financial decisions are made. Your work will directly influence how personalised, real-time financial insights are delivered, enabling smarter, more efficient advisory services and improving the overall customer experience. This is a unique opportunity to lead transformative AI solutions in a fast-growing sector.
Position: Lead Machine Learning Engineer
Salary: £80,000 - £105,000
Benefits: Equity + Benefits package
Location: Sherborne / Remote (occasional travel to London to be on client site)
Sector: FinTech
The day to day:
- Lead the development of innovative Generative AI models tailored to the wealth management industry.
- Drive the optimisation of large language models (LLMs) to extract deeper insights and enhance prediction capabilities for financial applications.
- Spearhead the implementation of Retrieval-Augmented Generation (RAG) systems, improving the AI's performance in specific financial scenarios.
- Lead initiatives for model fine-tuning, ensuring generative models perform optimally in real-world financial contexts.
- Design and build scalable AI pipelines capable of managing and processing complex financial data.
- Innovate in the field of Conversational AI, enhancing client-advisor interactions with intelligent, real-time decision-making systems.
- Develop and deploy AI-driven systems capable of real-time financial data analysis and actionable insights.
- Write clean, maintainable, and efficient code, establishing best practices for AI infrastructure within the company.
- Collaborate closely with cross-functional teams to integrate AI solutions into the core platform.
Essential skills / experience:
- 6+ years of experience as a Machine Learning Engineer, with a strong track record of impactful AI projects.
- Expertise in Generative AI, with at least 1 year of hands-on experience working with generative models.
- Strong experience with Retrieval-Augmented Generation (RAG) and other cutting-edge AI techniques.
- Proven success in fine-tuning models for specialized applications, particularly in financial services or data-driven domains.
- Advanced proficiency in Python and deep learning frameworks like PyTorch or TensorFlow.
- Strong experience with MLOps, ML pipelines, and deployment on cloud platforms like AWS, GCP, or Azure.
- Solid foundation in software engineering principles, ensuring that code is efficient, scalable, and maintainable.
Desirable skills / experience:
- Experience across a range of Generative AI models and architectures, with an understanding of their practical applications.
- Familiarity with the FinTech or wealth management sectors, and an understanding of the industry's unique challenges and opportunities.
- Contributions to the AI community, such as research papers, open-source projects, or speaking engagements.
- Knowledge of containerisation technologies like Docker and Kubernetes for seamless deployment.