Senior Software Engineer - Python, ML & AI Systems

Location: London, England, United Kingdom, Singapore, Singapore, Singapore

Department: Platform Engineering

Workplace: hybrid

Employment Type: full

Description

** Unfortunately, Cornspring isn't able to sponsor visas in the UK. You must have the right to work in the UK if you apply in London.

About us

Cornspring is an innovative FinTech start-up with a mission to empower Family Offices and Asset Owners with real-time, AI-driven data intelligence and portfolio insights.

We are tackling one of the most complex and valuable challenges in finance. Our clients operate at the highest levels of global markets, managing billions in assets, yet they remain constrained by legacy systems that are slow, fragmented, and outdated.

Cornspring is redefining Family Office services by applying state-of-the-art generative AI and Large Language Models to investment, accounting, and operational data. Our platform delivers faster insights, greater transparency, and entirely new ways of interacting with financial information.

The role

We are looking for a Senior Software Engineer with strong Python fundamentals and a focus on ML and LLM application. 

This is a key role where you will shape core system architecture and build production-grade systems end-to-end. You will work hands-on across the backend and applied AI layers, having real ownership over technical decisions and a direct impact on mission-critical products used by sophisticated financial clients. 

You’ll work on applying LLMs to highly fragmented, inconsistent financial data sources where correctness is critical.

Our Tech Stack 

• Languages: Python 
• Frameworks: FastAPI, Pydantic, Polars 
• AI/ML: HuggingFace, sentence-transformers, LangChain 
• Databases: PostgreSQL 
• Infrastructure: Azure, Docker, Github, Github Actions 
• Tooling : uv 

Key responsibilities

Backend Engineering & Architecture 

• Design and optimise scalable backend systems in Azure using modern architectural approaches. 
• Take ownership of services from concept to deployment, ensuring high performance through refactoring and asynchronous processing. 
• Collaborate with Product teams to transform complex financial requirements into robust features. 
• Work at pace: prototype rapidly, evaluate outcomes, and scale up successful approaches. 

ML & AI Systems 

• Design features using both SaaS LLMs and locally hosted / trained models (via HuggingFace/vLLM). 
• Build and evolve Retrieval-Augmented Generation (RAG) pipelines for complex structured and unstructured financial and accounting data. 
• Implement evaluation and monitoring for LLM outputs, prioritising correctness, latency, and cost efficiency. 
• Contribute to internal AI tooling to improve developer productivity. 

Technical Leadership 

• Champion clean architecture, maintainable code, and rigorous testing discipline. 
• Mentor fellow engineers through code reviews and technical discussions. 
• Drive ongoing improvements in system performance and the overall developer experience. 

Requirements

• Engineering Depth: Significant hands-on experience building production backend systems in Python. 
• ML & AI Experience: Strong experience building ML & AI-driven systems, specifically with LLMs and RAG pipelines (prompt engineering, model evaluation, orchestration). 
• Ownership Mindset: You are comfortable making architectural decisions and taking responsibility for the quality of your code. 
• Communication: You can explain complex technical concepts to non-technical stakeholders. 
• Educational background: Degree in Computer Science or equivalent, with strong Software Engineering fundamentals. 

Benefits

• Competitive Salary: A strong package that reflects your experience and contribution to our growth. 
• Health & Wellbeing: Private medical insurance, cash plan, life insurance, and income protection. 
• Hybrid Working: Three days per week in our collaborative City of London/Singapore office. 
• Impact: The opportunity to work at the forefront of AI-driven finance, tackling genuinely complex challenges in a high-ownership start-up environment.