AI Platform engineer
You'll work with Python (FastAPI), Go, TypeScript, PostgreSQL, Redis, Pinecone/pgvector, AWS (ECS, Lambda, SQS), Docker, Kubernetes, Terraform, LangChain/LlamaIndex, vLLM, and Anthropic/OpenAI APIs. You'll build and optimize RAG pipelines, real-time inference APIs, and vector search infrastructure powering LLM features for 500k+ monthly active users.
Free Tailor for ATS: 10/10 runs left
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