AI Platform engineer
Shoreditch, London
£65000 - £85000 per annum
Full Time
Backend Engineer - AI Infrastructure & Data Platforms
Location: London, UK (Hybrid - 2 days/week in office, Shoreditch)
Salary: £65,000-£85,000 + equity
Type: Full-time, Permanent
About the Role
We're scaling the backend systems powering our AI-driven product suite, including retrieval-augmented generation (RAG) pipelines, real-time inference APIs, and vector search infrastructure. You'll own the services that sit between our LLM providers and our customer-facing products, focusing on reliability, latency, and cost efficiency at scale.
What You'll Do:
- Design and maintain APIs serving LLM-powered features (chat, search, agentic workflows) to 500k+ monthly active users
- Build and optimise RAG pipelines, including chunking strategies, embedding generation, and vector store management
- Architect event-driven microservices using Kafka/SQS for async processing of high-volume inference requests
- Implement observability and cost-tracking for token usage across multiple LLM providers (Anthropic, OpenAI, open-source models via vLLM)
- Own database performance for both relational (Postgres) and vector (pgvector/Pinecone) workloads
- Collaborate with ML engineers on model-serving infrastructure and prompt-caching strategies
- Drive API versioning and backwards compatibility as the product iterates quickly
Core Tech Stack:
- Languages: Python (FastAPI), Go, TypeScript (Node.js)
- Databases: PostgreSQL, Redis, Pinecone/pgvector
- Infra: AWS (ECS, Lambda, SQS/SNS), Docker, Kubernetes, Terraform
- AI/ML tooling: LangChain/LlamaIndex, vLLM, Anthropic & OpenAI APIs, embedding models
- Observability: Datadog, Grafana, OpenTelemetry
- CI/CD: GitHub Actions, ArgoCD
Requirements:
- 4+ years backend development experience, ideally with at least 1 year working with LLM/AI-integrated systems
- Strong grasp of asynchronous architecture and distributed systems fundamentals
- Experience with vector databases or semantic search implementations
- Comfortable working directly with LLM APIs (rate limits, streaming responses, token cost optimisation)
- Solid understanding of API design, security, and scalability best practices
Nice to Have:
- Experience fine-tuning or evaluating open-source models
- Familiarity with agentic frameworks (function calling, tool use, MCP)
- Prior startup/scale-up experience
Benefits:
- 25 days holiday + bank holidays
- Private healthcare (Vitality)
- £1,000 annual learning budget
- Hybrid working with full WFH equipment provided