Backend Software Engineer Python LLM - Finance
Backend engineer at a finance technology consultancy serving global investment banks, designing autonomous AI systems: multi-modal LLM integration, agentic workflows with RAG and prompt engineering, model optimisation, and scalable MLOps pipelines. Core stack is Python with API development and cloud-native AI deployment.
- Compensation
- £75K–£90K / yr
- Location
- London, United Kingdom
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- 2.1 or above BSc degree in Computer Science, AI or related STEM disciplines
- Strong backend Python software engineering background including API development
- Hands-on experience deploying LLMs and multi-modal models at scale in production
- Strong understanding of scalable MLOps, observability and cloud-native AI deployment
- Experience within finance or consultancy environments
- Advanced stakeholder communication, problem-solving and project management skills in Agile environments
Nice to have
- Agentic frameworks (e.g. LangChain, LlamaIndex)
- Deep learning frameworks
- Langfuse
- Langsmith
- MCP (Model Context Protocol)
- Bias mitigation techniques
Full description
London, Greater London | £75k - £90k per year
Backend Software Engineer / Developer (Python LLM MLOps) London / WFH to £90k
Are you an experienced Backend Software Engineer with strong Python skills and a good knowledge of AI, LLMs and agentic coding?
You could be progressing your career at a global finance technology consultancy, working with clients including global Investment Banks and financial services organisations.
What's in it for you:
- Salary to £90k + bonus
- Pension, Life Assurance, Income Protection
- Private medical care for you and your family, including mental health
- Travel Insurance
- Charitable giving
- Gym membership for you and your family
Your role:
As a Backend Software Engineer you'll design and develop autonomous AI systems integrating multi-modal LLMs (text, image, audio, video), design agentic workflows enabling AI agents to interact with data sources and APIs using prompt engineering and RAG, optimise and deploy large language and multi-modal models with a focus on performance and latency and build scalable MLOps pipelines and full-stack applications to support robust, production grade AI deployments.
Using a consultative approach, you'll guide multidisciplinary teams and clients through strategic decisions in AI engineering and GenAI adoption.
About you:
- You have achieved a 2.1 or above BSc degree in Computer Science, AI or related STEM disciplines
- You have a strong backend Python software engineering background including and API development
- You have hands-on experience deploying LLMs and multi-modal models at scale in production
- You have a strong understanding of scalable MLOps, observability and cloud-native AI deployment
- You have experience within finance or consultancy environments
- You're collaborative and pragmatic with advanced stakeholder communication, problem-solving and project management skills in Agile environments
- Experience with any of the following is advantageous: agentic frameworks (e.g., LangChain, LlamaIndex), deep learning frameworks, Langfuse, Langsmith, MCP (Model Context Protocol), bias mitigation techniques