6 of 6 Google Gemini Jobs in Central London

in GenAI or LLM-based solution design. Proven experience designing AI solutions for banks, asset managers, insurers, or fintechs. Expertise across a wide spectrum of GenAI technologies (e.g., OpenAI, Google Gemini, Anthropic, Meta Llama, Hugging Face). Familiarity with financial data sources (e.g., market data, transactional data, credit reports) and core systems (e.g., core banking, CRMs, compliance systems More ❯

for internal systems and customer-facing applications Scaling the platform to support heavy, multi-tenant usage patterns and high-throughput AI workloads Applying foundation models (GPT-4.1, Claude 4, Gemini 1.5 and successors) to real-world, multi-modal data challenges Shipping to production daily in collaboration with Product and Design to solve real user problems Playing an active role More ❯

scalable AI services that power next-generation use cases across client intelligence, document processing, and risk management. You'll work in a greenfield environment, building compliant AI pipelines using Gemini (GCP), Azure OpenAI or Self Hosting embedding security and privacy controls from experimentation to production, in alignment with the bank's cybersecurity and regulatory standards. Accountabilities & Responsibilities Architect and … Worked directly with cybersecurity and compliance teams in regulated deployments Implemented or maintained controls under ISO 27001, NIST, or SOC2 frameworks Technical Skills & Technologies: Languages & Frameworks Python (FastAPI), LangChain, Google AI SDK, Azure Open AI SDK Cloud & AI Platforms GCP: Vertex AI, Gemini API, Cloud Run, GCS, IAM, Secret Manager, Audit Logs Azure: Azure ML, Azure OpenAI, Key … Vault, Azure Policy Experience with Self Hosting LLM Fine-tuning and prompt engineering for LLMs (e.g., GPT, Gemini, Claude) Secure deployment of LLMs via APIs with input/output filtering and logging Integration of LLMs into RAG pipelines, document intelligence, and agentic workflows Use of vector databases (e.g., FAISS, Pinecone, Chroma) for semantic search and retrieval Implementation of grounding More ❯

Responsibilities Design and develop modern, responsive front-end applications from the ground up using React, TypeScript, and Next.js. Build secure, performant user interfaces that integrate with LLM APIs (e.g., Gemini, Azure OpenAI) and internal systems. Implement advanced front-end security features including token handling, session management, data masking, and audit logging. Translate UX/UI designs into modular, reusable … CSS UI Libraries: MUI, Ant Design, Tailwind CSS, AG Grid, Highcharts or similar State Management: Redux Toolkit, React Query, Zustand or similar AI/Cloud Integration: GCP: Vertex AI Gemini API, Firebase Hosting, Google IAM, Secret Manager Azure: Azure OpenAI, Azure App Services, Azure AD authentication Cybersecurity: CSP headers, XSS/CSRF protection, OAuth2 flows, JWT encryption Front … and audit trails Familiarity with OWASP guidelines and secure SDLC practices DevOps: GitHub Actions, Docker, Terraform, security scans in CI/CD (e.g., Snyk, Dependabot or similar). Monitoring: Google Cloud Operations Suite, Azure Monitor More ❯

and efficiency within our global financial services operations. Core Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous … Parameter-Efficient Fine-Tuning) and QLoRA, to adapt large language models for specialized financial tasks. Deploy Cloud-Native AI Solutions: Build, deploy, and manage scalable AI applications on the Google Cloud Platform (GCP), with a strong focus on Vertex AI services. Build Scalable Backend Services: Develop secure, high-performance APIs and microservices using Python and FastAPI to integrate agentic … systems into the wider enterprise architecture. Technical Skillset Requirements Core AI & Frameworks: Agentic Frameworks: Expert-level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector More ❯

and efficiency within our global financial services operations. Core Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous … Parameter-Efficient Fine-Tuning) and QLoRA, to adapt large language models for specialized financial tasks. Deploy Cloud-Native AI Solutions: Build, deploy, and manage scalable AI applications on the Google Cloud Platform (GCP), with a strong focus on Vertex AI services. Build Scalable Backend Services: Develop secure, high-performance APIs and microservices using Python and FastAPI to integrate agentic … systems into the wider enterprise architecture. Technical Skillset Requirements Core AI & Frameworks: Agentic Frameworks: Expert-level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector More ❯