GenAI Platform Engineer

Description

You will be instrumental in designing and implementing our award winning Generative AI Platform Model Orchestration Layer, RAG infrastructure, communications layer, agentic layer and centralised governance/safety guardrails etc.

• ng.Drive the quality of the developer experience by writing exceptional SDKs, clear APIs, comprehensive documentation, and contributing to internal knowledge sharing (e.g. through workshop
• ls.Engineer and maintain production-ready Vector Database and Retrieval-Augmented Generation (RAG) infrastructure, including high-throughput indexing pipelines and efficient retrieval strategies for enterprise da
• n).Ensure the platform and RAG data handling adheres to strict compliance and data privacy regulations (e.g., GDPR) through automated enforcement and auditi
• rm.Implement robust mechanisms for cost tracking, monitoring, and precise cost allocation/chargebacks per squad, alongside implementing rate-limiting and budget contro
• es.Build and maintain scalable, secure infrastructure and data pipelines for custom model fine-tuning requested by product tea
• ngDesign, build, and maintain the highly reliable Model Serving Layer (Gateways, load balancing, caching, throttling) for low-latency access to LLMs and other generative mode
• utsBuild and maintain the centralised logging, monitoring, and observability stack to track crucial GenAI metrics (hallucination rate, latency, token usage, and drift detectio
• ls.Systematically benchmark, curate, and integrate the most cost-effective and performant models for distinct use cas
• ta.Develop and manage a standardised, secure Agent Framework/SDK (e.g., based on LangGraph or a custom library) that promotes consistency and maintainability across all agents built from the platfo
• ms.Architect and deploy platform-level safety filters (e.g., toxicity, PII masking, jailbreak prevention) that are enforced across all model inputs and outp

Requirements

• low).Proven experience designing, implementing, and optimising complex RAG pipelines using vector databases (e.g., Pinecone, PGVec
• etes.Understanding of LLM architectures, inference serving optimization techniques (quantisation, caching), and MLOps tooling (e.g., MLFlow, Kubef
• tch?Minimum 2 years of experience in Software Engineering, ML Engineering, Data Science, or Data Engineering with a (bonus) specialised focus on building Generative AI products or agent-based sys
• tor).Practical experience with agentic frameworks (e.g., LangChain, LlamaIndex, Langgraph, or custom implementation) and defining tool-use patt
• ents.A strong commitment to improving Developer Experience (DX) and acting as a technical consultant for internal product t
• ment.Extensive, hands-on experience with cloud infrastructure (AWS, GCP, or Azure), Infrastructure as Code (Terraform), Docker - containerisation. An understanding of kubern
• tems.Proficiency in Python and developing highly concurrent, scalable API services and microservices in a cloud environ
• erns.Proven ability to engineer for operational efficiency, with a track record of implementing cost-saving measures in large-scale cloud deploym
• erns.Demonstrated experience designing complex, multi-tenant platform systems, prioritising reliability, security, and separation of conc

Benefits

This is a hybrid role, typically working 1 day per week at our Citigen office in London

Training + Development

Interview process

Visa Sponsorship

Security clearance