Senior Full-Stack Engineer - AI Agent Platform
Greater London, London, Greater London, W1B, England, UK
Listed on 2026-06-12
-
Software Development
Cloud Engineer - Software, Software Engineer, Backend Developer, AI Engineer (Applied/Software)
Ebury helps ambitious businesses unlock global growth, and we take the same approach with our people. We encourage innovation and movement, collaboration and problem-solving, and foster an environment where everyone can feel they belong, are valued, supported and empowered to succeed.
If you’re a collaborator who wants to help transform how businesses operate globally, get in touch – we’d love to discuss how Ebury can accelerate your career so you can shape the future.
Senior Full-Stack Engineer - AI Agent Platform London Victoria Office - Hybrid: 4 days in the office, 1 day working from home AboutThe Role
We’re building an AI‑powered financial crime investigation platform that’s transforming how Ebury’s compliance teams work. Our AI agents will process thousands of screening cases, reducing analyst workload while maintaining regulatory standards. We’re looking for a Senior Full-Stack Engineer to help us scale our platform and raise the engineering bar. You’ll architect and deliver complex features across the entire stack – from React frontends to Python backends to LLM integrations – while mentoring teammates and shaping our technical direction.
This is a unique opportunity to work at the intersection of AI/ML, fintech, and compliance – building production AI systems that handle real financial crime investigations at scale.
- Architect and build end‑to‑end features across our React frontend and FastAPI backend, making sound technical decisions that balance speed with maintainability
- Design AI agent workflows using Lang Graph for screening automation, RAG pipelines, and multi‑agent orchestration
- Lead technical initiatives – own complex features from design to production, breaking down ambiguous problems into actionable work
- Integrate complex data sources (internal APIs, third‑party services like Lexis Nexis, data warehouse) with robust error handling and observability
- Improve platform reliability – implement monitoring, alerting, and performance optimizations for production AI systems
- Mentor and uplift junior and mid‑level engineers through code reviews, pairing, and technical guidance
- Shape engineering practices – contribute to architecture decisions, coding standards, and team processes
- Collaborate cross‑functionally with Product, Design, Compliance, and other engineering teams to deliver impactful solutions
- React, Type Script, Material‑UI
- Module Federation (Micro‑frontend architecture)
- Vite, Vitest
- Python 3.13, FastAPI
- Lang Graph (AI agent orchestration)
- Llama Index (RAG/vector search)
- AWS Bedrock
- Dynamo
DB, Open Search Serverless
- AWS (ECS Fargate, Lambda, API Gateway, S3)
- Terraform, Git Hub Actions
- Docker, Kubernetes
- Sentry, Langfuse (LLM observability)
- 5+ years of professional software engineering experience
- Strong React + Type Script skills – you can architect complex frontend applications with good state management, performance, and testing
- Strong Python backend experience – FastAPI, Django, or Flask with async programming, clean architecture, and production‑grade code
- System design skills – you can design scalable, maintainable systems and articulate trade‑offs clearly
- API design expertise – REST, Web Sockets/SSE, and understanding of distributed systems patterns
- Database proficiency – SQL and No
SQL (Dynamo
DB, Postgre
SQL, Open Search), including query optimization and data modelling - Testing mindset – you write comprehensive tests and advocate for quality across the team
- Ownership mentality – you take features from idea to production, proactively identifying and solving problems
- Strong communication – you can explain complex technical concepts to engineers and non‑engineers alike, and write clear documentation
- Mentorship ability – you enjoy helping others grow and have experience guiding less experienced engineers
- LLM/AI experience – Lang Chain, Lang Graph, RAG pipelines, prompt engineering, or fine‑tuning
- Production AI systems – experience with LLM observability (Langfuse), token management, streaming, and reliability patterns
- AWS expertise – Lambda, ECS, Dynamo
DB, Open Search, Bedrock, or similar cloud services - Streaming architectures – SSE, Web…
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