2 months ago
London, United KingdomSenior
Responsibilities
- Design, build, and evolve the enterprise AI platform supporting AI products across 9fin.
- Develop scalable services for agentic AI, RAG, embeddings, vector search, model serving, and intelligent automation.
- Build orchestration layers that transform structured and unstructured enterprise data into reusable AI capabilities.
- Develop production-ready AI applications using LLM frameworks and orchestration tools.
- Create reusable components for prompt management, model serving, vector search, embeddings, AI gateways, and evaluation services.
- Build APIs, SDKs, and developer tooling for self-service AI development.
- Design secure deployment pipelines and observability capabilities for AI models and applications.
- Collaborate with AI engineers, backend engineers, and engineering leadership on platform architecture, standards, governance, testing, Responsible AI, and operational excellence.
- Evaluate emerging AI technologies and help shape the platform’s technical direction.
Requirements
- 5+ years of software engineering experience, including 2+ years building AI/ML platforms, generative AI applications, or production machine learning systems.
- Experience designing and deploying scalable, reliable enterprise AI applications into production.
- Hands-on experience building applications powered by large language models and agentic AI systems.
- Experience with Model Context Protocol, RAG, embeddings, vector databases, and modern LLM orchestration frameworks.
- Strong backend engineering experience with Python and/or TypeScript, scalable backend services, REST APIs, and event-driven architectures.
- Experience building reusable platform capabilities, internal developer tooling, SDKs, or shared engineering services.
- Experience with cloud-native applications, Docker, Kubernetes, CI/CD pipelines, and containerized deployments.
- Experience with AWS Bedrock and AgentCore or similar solutions.
- Experience implementing monitoring, tracing, evaluation, and cost optimization for AI systems, including tools such as Arize Phoenix, Langfuse, or LangSmith.
- Understanding of production LLM application challenges including latency, reliability, hallucination monitoring, and model quality evaluation.
- Experience or interest in AI gateways, model routing, prompt management, evaluation frameworks, LangGraph, LangChain, LlamaIndex, DSPy, CrewAI, knowledge graphs, document understanding systems, or internal AI platforms.
Benefits
- Competitive salary, equity, pension with 9fin matching up to 7%, private medical insurance, paid sick leave with income protection, and group life assurance.
- Season ticket loan and Cycle to Work schemes.
- 25 holiday days per year plus local public holidays, with the option to exchange holidays for alternative days.
- Hybrid working with flexibility over where and when to work, and the option to work abroad for up to 3 months per year.
- One month of paid sabbatical after 5 years, enhanced parental leave, and flexible working arrangements.
- Professional learning and development budget, an £800 UK AI experimentation budget, and company social events.
Tech Stack
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