26 to 50 of 2,239 LLM Jobs in London

Generative AI use cases. Design and build APIs and microservices that integrate AI capabilities into internal and external workflows. Develop, fine‐tune, and deploy LLM‐based applications (e.g., chatbots, assistants, knowledge extraction tools). Architect scalable AI infrastructure on cloud platforms (AWS, GCP, or Azure), leveraging containerisation and IaC. Implement ...

Generative AI use cases. Design and build APIs and microservices that integrate AI capabilities into internal and external workflows. Develop, fine-tune, and deploy LLM-based applications (e.g., chatbots, assistants, knowledge extraction tools).Architect scalable AI infrastructure on cloud platforms (AWS, GCP, or Azure), leveraging containerisation and IaC.Implement CI/ ...

related role. Strong practical experience using generative AI tools to improve analysis, documentation, coding, research, workflow automation, or business productivity. Working knowledge of LLM concepts, including prompting, context windows, retrieval, hallucination risk, structured outputs, model selection, and evaluation tradeoffs. Familiarity with applied AI patterns such as RAG, embeddings, vector search ...

experience with PyTorch, TensorFlow, and Hugging Face Experience taking AI proof-of-concepts from idea to production Strong knowledge of deep learning, NLP, and LLM architectures Experience fine-tuning LLMs or training domain-specific models, and with RAG, embeddings, and vector databases Familiarity with agentic AI concepts and orchestration frameworks ...

Preferred qualifications, capabilities, and skills Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector ...

Hugging Face, OpenAI APIs etc) Experience in building and optimising systems for performance, scalability, and reliability Understanding of ML fundamentals (training, evaluation, deployment) and LLM‐specific challenges (prompt engineering, latency, cost optimisation, hallucinations, evaluation) Experience with cloud platforms (AWS, GCP, Azure) and infrastructure‐as‐code (Terraform etc). Hands ...

advanced analyticsCloud & Data Platforms – Microsoft Fabric, Databricks, Snowflake, Azure, AWS and GCPAI Technologies – GenAI frameworks, Agentic AI, vector databases, semantic search, knowledge retrieval and LLM application developmentData & Visualisation – Power BI, Tableau, Looker and other business intelligence toolsConsulting, Communication & Leadership:Ability to work effectively with both business and technical stakeholders.Strong communication ...

Senior AI Engineer/AI Engineering Consultant/Generative AI Consultant/Senior Machine Learning Engineer/AI & ML Consultant/MLOps Engineer/LLM Engineer/AI Engineering Consultant/Senior AI Engineer/Generative AI Engineer/AI Solutions Architect/Data & AI Consultant/AI Solutions Consultant ...

Senior AI Engineer/AI Engineering Consultant/Generative AI Consultant/Senior Machine Learning Engineer/AI & ML Consultant/MLOps Engineer/LLM Engineer/AI Engineering Consultant/Senior AI Engineer/Generative AI Engineer/AI Solutions Architect/Data & AI Consultant/AI Solutions Consultant ...

other relevant enterprise platforms. Understand the foundations required for safe and scalable AI delivery, including identity, permissions, environments, data lineage, observability, evaluation, auditability, LLM access patterns and responsible AI controls. Work closely with Data Solutions & Architecture colleagues to ensure AI use cases are built on the right data foundations. Understand ...

part of a broader project team, remotely or face to face, and confident challenging established approaches. Desirable Experience: Experience running AI/LLM solutions in production at scale, including evaluation frameworks, guardrails and cost management. Experience designing or orchestrating multi-agent workflows, and familiarity with agent protocols (e.g. ...

interact with Moody's platforms and third-party services via MCP and related protocols, and define the patterns other engineers build againstImplement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integrationBuild and deliver compelling proofs of concept, live demonstrations ...

Drive – the operating manual for how we behave. AI & Machine Learning Experience Deploying and operating machine learning models within enterprise environments. Experience with LLM-powered applications, agent frameworks and retrieval systems. Familiarity with prompt engineering, evaluation frameworks and AI safety controls. Experience with reinforcement learning, fine-tuning or model customisation ...

testing, automation, or developer productivity use cases. This may include experience with: AI application patterns Retrieval-Augmentation Generation (RAG)prompt engineeringtool callingagentic workflowsevaluation of LLM-based outputs Frameworks and orchestration LangChainLangGraphCrewAI Models and platforms OpenAI and/or Azure OpenAI Vector search and retrieval Weaviate or equivalent vector database technology ...

Agentic AI systems into production environments Expertise in Python engineering for APIs, microservices, testing and CI/CD workflows Strong working knowledge of LLM capabilities, including prompt design, structured outputs, tool calling and retrieval strategies Hands-on experience with agent orchestration frameworks (LangGraph, AutoGen, CrewAI or Semantic Kernel) Proven experience ...

DevOps: Experience with Kubeflow, Docker, Kubernetes, and Linux environments. CI/CD: Proficiency in building and managing continuous integration and deployment pipelines. GenAI/LLM: Experience with LLMs, GenAI APIs, Model Garden, and GenAI Studio. Good to Have Experience with AlloyDB and enterprise database solutions. Familiarity with PaLM embeddings ...

reliable production delivery. Preferred Skills Experience bringing AI/ML capabilities into production, such as model deployment and monitoring, ML pipelines, model serving, or LLM-powered applications and retrieval-augmented generation (RAG).Cloud and platform engineering experience with AWS, Azure or GCP; Docker or Kubernetes; Infrastructure as Code such ...

thought leadership. What we’re looking for Deep software/systems engineering (Python/TypeScript, distributed systems, CI/CD). Applied‐AI expertise: LLM/RAG engineering; evaluation; telemetry/drift monitoring; versioning and release management. Cloud architecture (Azure/AWS/GCP), Kubernetes/Docker, serverless ...

/or Java and comfortable working across backend services, APIs, data, and application architecture. You have built and shipped at least one agent or LLM-powered product capability end to end. You have personally integrated agents into a broader product and backend architecture - not only developed standalone prototypes. You have ...

APIs, tooling, and bespoke logic Architecture & Standards Architect robust, scalable, and compliant Agentic AI solutions across industries Lead multi-agent orchestration, RAG pipelines, and LLM/SLM integration design Champion observability, telemetry, and auditable AI standards Build and maintain reusable frameworks and asset libraries Team & Product Help build and guide ...

Systems, or a related field, or equivalent practical experience. Machine Learning: Expertise in time‐series forecasting, clustering, and correlation algorithms. Generative AI: Experience with LLM prompting, Retrieval‐Augmented Generation (RAG), and LangChain. Cloud Infrastructure: Deep understanding of AWS, Azure, or GCP infrastructure components Additional Preferred Qualifications: Experience with enterprise observability ...

pipelines for infrastructure and applications Agent Engineering & Development: 1+ years of experience building production AI/ML applications or agents Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications Experience with state management patterns (short-term and long-term memory) Experience designing and implementing evaluation ...

technology developments.Take on related responsibilities as the squad's needs evolve.Requirements: Engineering experience: 3+ years in software engineering, with hands-on experience building LLM-powered applications in production (RAG, tool-augmented agents, or agentic workflows).Education: BS in Engineering, Computer Science, or equivalent.Ways of working: Comfortable working autonomously within ...

developments. Take on related responsibilities as the squad's needs evolve. Requirements: Engineering experience: 3+ years in software engineering, with hands-on experience building LLM-powered applications in production (RAG, tool-augmented agents, or agentic workflows). Education: BS in Engineering, Computer Science, or equivalent. Ways of working: Comfortable working ...

taking AI prototypes to production in enterprise environments. Education: BS in Engineering, Computer Science or equivalent; advanced degree preferred. Agentic AI/LLMs: LLM orchestration, prompt engineering, context‐window management and production guardrails; MCP, tool‐augmented agents and RAG at enterprise scale. Enterprise integration: Integrating AI into enterprise systems ...