GenAI Platform Engineer

Job DescriptionWe are maintaining and enhancing our Generative AI Platform that enables our entire organisation to leverage Generative AI safely, efficiently and at scale. As a Generative AI Platform Engineer, you will be the force multiplier, moving beyond building end-user applications to developing robust, centralised services that power all internal GenAI product squads.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.This is a hybrid role, typically working 1 day per week at our Citigen office in London.Here’s a taste of what you’ll be doingDesign, build, and maintain the highly reliable Model Serving Layer (Gateways, load balancing, caching, throttling) for low-latency access to LLMs and other generative models.Engineer and maintain production-ready Vector Database and Retrieval-Augmented Generation (RAG) infrastructure, including high-throughput indexing pipelines and efficient retrieval strategies for enterprise data.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 platform.Implement robust mechanisms for cost tracking, monitoring, and precise cost allocation/chargebacks per squad, alongside implementing rate-limiting and budget controls.Systematically benchmark, curate, and integrate the most cost-effective and performant models for distinct use cases.Build and maintain scalable, secure infrastructure and data pipelines for custom model fine-tuning requested by product teams.Architect and deploy platform-level safety filters (e.g., toxicity, PII masking, jailbreak prevention) that are enforced across all model inputs and outputsBuild and maintain the centralised logging, monitoring, and observability stack to track crucial GenAI metrics (hallucination rate, latency, token usage, and drift detection).Ensure the platform and RAG data handling adheres to strict compliance and data privacy regulations (e.g., GDPR) through automated enforcement and auditing.Drive the quality of the developer experience by writing exceptional SDKs, clear APIs, comprehensive documentation, and contributing to internal knowledge sharing (e.g. through workshops).Are we the perfect match?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 systems.Proficiency in Python and developing highly concurrent, scalable API services and microservices in a cloud environment.Extensive, hands-on experience with cloud infrastructure (AWS, GCP, or Azure), Infrastructure as Code (Terraform), Docker - containerisation. An understanding of kubernetes.Understanding of LLM architectures, inference serving optimization techniques (quantisation, caching), and MLOps tooling (e.g., MLFlow, Kubeflow).Proven experience designing, implementing, and optimising complex RAG pipelines using vector databases (e.g., Pinecone, PGVector).Practical experience with agentic frameworks (e.g., LangChain, LlamaIndex, Langgraph, or custom implementation) and defining tool-use patterns.Demonstrated experience designing complex, multi-tenant platform systems, prioritising reliability, security, and separation of concerns.Proven ability to engineer for operational efficiency, with a track record of implementing cost-saving measures in large-scale cloud deployments.A strong commitment to improving Developer Experience (DX) and acting as a technical consultant for internal product teams.