Barclays

AI platform engineer for inference routing

In short

Lead-level AI Platform Engineer role at Barclays in London, owning core enterprise AI infrastructure subsystems like AI Gateway, inference routing, guardrails, and observability. The role combines platform architecture with hands-on engineering and mentoring, requiring strong cloud-native distributed systems experience, deep Python/Go/Java skills, AWS and Kubernetes at scale, and GenAI/Agentic AI technologies. It's a full-time, onsite position for a bank with a substantial enterprise AI infrastructure build-out.

About the Role
Join Barclays as an AI Platform Engineer, where you will design and build core components of our enterprise AI infrastructure. You will own key platform subsystems, including the AI Gateway, inference routing, guardrails, and observability, delivering secure, scalable, and production-grade solutions aligned with our architectural vision. This role combines platform architecture with hands-on engineering, enabling you to drive technical delivery while mentoring junior engineers.
Responsibilities
•Development and delivery of high-quality software solutions using industry-aligned programming languages, frameworks, and tools
•Ensuring that code is scalable, maintainable, and optimized for performance
•Cross-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives
•Collaboration with peers, participation in code reviews, and promoting a culture of code quality and knowledge sharing
•Staying informed of industry technology trends and innovations and actively contributing to the organization's technology communities to foster a culture of technical excellence and growth
•Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions
•Implementation of effective unit testing practices to ensure proper code design, readability, and reliability
Requirements
•Cloud-native distributed systems — designing and owning platform subsystems end-to-end
•Extensive Python (FastAPI/asyncio) or Go/Java skills — building production APIs and microservices
•AWS and Kubernetes at scale — including EKS, Bedrock, IAM, RDS/Aurora, deployment strategies, and HPA
•GenAI and Agentic AI technologies — including LangGraph/LangChain or Google ADK, MCP/A2A, RAG pipelines, and LLM integration
Nice to Have
•Envoy Proxy or API gateway platforms
•OPA/Rego policy-as-code
•AI safety controls, including guardrails, prompt injection defence, and content filtering
•Zero Trust and identity systems, including EntraID and OAuth2/OIDC
Conditions
No conditions specified in the posting.

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