Senior AI Engineer

2 months ago

London, United KingdomSenior

Responsibilities

Architect and implement multi-agent systems using Google AI SDKs, Vertex AI Agent Builder, LangGraph, CrewAI, and related orchestration frameworks.
Build production-grade LLM applications including RAG pipelines, specialized agents, developer copilots, and tool-augmented workflows.
Design agent memory, planning, routing, prompting, guardrails, and cross-agent communication patterns.
Develop reusable AI microservices, agent frameworks, standardized APIs, model routing, observability, and safety capabilities.
Deploy and manage AI systems across GCP, Azure, and AWS using Docker, Kubernetes, and Cloud Run.
Implement monitoring and observability, optimize latency, token cost, and quality, and lead incident response and post-mortems.
Provide technical leadership, mentor junior engineers, conduct code reviews, and shape AI engineering architecture and standards.

5–8 years of software engineering experience, including at least 3 years focused on production LLM-based or AI systems.
Proven experience building and shipping RAG pipelines, autonomous agents, and multi-step reasoning chains.
Strong hands-on experience with Google AI SDKs, Vertex AI, and/or Azure AI services.
Deep proficiency with LangGraph, CrewAI, LlamaIndex, Haystack, or comparable orchestration frameworks.
Expert-level Python and strong backend development experience with FastAPI, Go, or Node.js.
Experience with agent planning, reflection, memory, tool use, enterprise API integration, event-driven systems, and debugging non-deterministic AI workflows.
Familiarity with GCP and/or Azure services, including GKE, Cloud Run, and Azure AI services.
Experience with Terraform or Pulumi and automated AI model evaluation and deployment pipelines.
Preferred experience with vector databases, data pipelines, and ETL/ELT for RAG and fine-tuning data.
Ability to take technical ownership, mentor engineers, and build resilient, observable, production-ready AI systems.

501-1,000 employees