1 to 25 of 2,239 LLM Jobs in London

grade Generative AI applications, including:* Retrieval-Augmented Generation (RAG)* AI Agents and Agentic Workflows* Prompt Engineering and Prompt Optimization* Model Evaluation and Observability Frameworks* LLM Fine-tuning and Adaptation Techniques* Hands-on experience with Azure AI Foundry, Azure OpenAI Service, Model Context Protocol (MCP), and modern AI orchestration frameworks.* Strong ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale • Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration • Make key technical decisions while remaining accountable ...

encompassing:Compute & Services: Java, .NET, Python, Cloud-native microservicesData & Analytics: Databricks, Microsoft Fabric, AWS Glue, Athena, Event Streaming (e.g., Kafka)AI & ML: Azure OpenAI, LLM Integration patterns, Vector DatabasesIntegration & Storage: Azure Data Factory (ADF), SQL Server, Enterprise relational platformsInfrastructure: Azure, AWS, and Hybrid Cloud networksCandidates are not expected ...

tool-calling frameworks (e.g. LangGraph or equivalent), including familiarity with the Model Context Protocol (MCP)• Evaluation engineering (golden datasets, regression gates in CI, LLM-judge calibration)• Guardrail and AI-observability tooling (e.g. NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust)MLOps & LLMOps• Hands-on with MLOps platforms (Azure ML, Databricks, SageMaker ...

field, or demonstrable relevant experience. Additional desired skills Knowledge of vector databases and retrieval strategies. Experience with recommender systems and ranking models. Familiarity with LLM evaluation tools (e.g., RAGAS, TruLens, LangSmith, Arize). A working understanding of cloud networking and platform infrastructure. Experience in e‐commerce or retail environments. ...

agentic AI systems—tool use, orchestration, multi‐step reasoning, or agent frameworks (e.g. LangGraph, LangChain, CrewAI, AutoGen, or similar). Experience with GenAI/LLM systems in a production context (RAG, prompt orchestration, evaluation, guardrails, cost/latency optimisation). Exposure to MCP (Model Context Protocol) and tool‐based/ ...

learning.Proficiency in Python for data analysis, model development, and deployment.Strong experience with transformer modelsProficiency in Generative AI technologies, including utilizing LLMs via API access, LLM evaluation tools, and prompt engineering.Knowledge of various RAG pipelines and their practical implementation.Experience building Agentic RAG systems is strong requirement.Experience with AI agent management frameworks ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real‐time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval‐augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

Solution Design: Design, build, and maintain scalable AI architectures and pipelines that support business requirements, ranging from predictive modelling to Generative AI applications. LLM Integration: Operationalise Large Language Models (e.g., GPT, Llama, Mistral) and NLP workflows (BERT, topic modelling) for production environments. Agentic & Multi-Agent Systems: Design and orchestrate autonomous ...

Django, Flask, or FastAPI Experience with API development, integration, and web services in production environments Hands‐on experience integrating AI or ML models, LLM APIs, or automation frameworks into applications Strong knowledge of database systems including SQL, NoSQL, and data storage patterns for AI applications Familiarity with cloud platforms such ...

productionising agentic AI systems—tool use, orchestration, multi-step reasoning, or agent frameworks (e.g. LangGraph, LangChain, CrewAI, AutoGen, or similar).Experience with GenAI/LLM systems in a production context (RAG, prompt orchestration, evaluation, guardrails, cost/latency optimisation).Exposure to MCP (Model Context Protocol) and tool-based/function ...

such as LangGraph, CrewAI, Hugging Face or OpenAI APIs) and ML frameworks like PyTorch or TensorFlowStrong understanding of ML fundamentals (training, evaluation, deployment) and LLM-specific challenges such as prompt engineering, latency and cost optimisationExperience with cloud platforms (AWS, GCP or Azure) and infrastructure-as-code such as TerraformHands ...

delivery through reusable assets, frameworks, and documentation. Champion observability, monitoring, versioning and telemetry to ensure trustworthy and auditable AI agents. Stay current on RAG, LLM/SLM advancements, Agentic AI tooling, vector/graph databases, retrieval optimizations, multi‐modal systems, evaluation frameworks, GPU/Infra trends, fine tuning techniques, security ...

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 ...

RBAC and Cost Controls.Software Engineering: Clean Code (SOLID, DRY), DDD, modular architecture, TDD, code quality tools. · AI Engineering (Desirable): RAG pipelines, agentic workflows, MCP, LLM integration (OpenAI, Gemini or other models from Huggingface), LangSmith, TruLens.Agentic Frameworks (Desirable): LangChain, CrewAI, Autogen, OpenAI Agents, Google ADK.WHAT YOU'LL LOVE ABOUT WORKING HEREWe ...

evaluation, packaging, and production deployment. Deploy Large Language Models (LLMs) and generative AI workloads using advanced inference engines (e.g., Triton Inference Server, vLLM, TensorRT-LLM). Enable automated model validation, monitoring for model drift, data drift, and latency bottlenecks. Monitor and optimise cloud spend across high-cost GPU/ ...

know yet). You’ll cover: Foundations of machine learning AI literacy Ensemble methods & model optimisation Databases & big data Neural networks & deep learning LLM foundations, engineering, customisation and integration Generative and Agentic AI MLOps & cloud computing It’s all live lectures, tutor-led group work and real-world challenges from ...

evaluation, packaging, and production deployment. Deploy Large Language Models (LLMs) and generative AI workloads using advanced inference engines (e.g., Triton Inference Server, vLLM, TensorRT-LLM).Enable automated model validation, monitoring for model drift, data drift, and latency bottlenecks. Monitor and optimise cloud spend across high-cost GPU/CPU clusters ...

Solution Design: Design, build, and maintain scalable AI architectures and pipelines that support business requirements, ranging from predictive modelling to Generative AI applications. LLM Integration: Operationalise Large Language Models (e.g., GPT, Llama, Mistral) and NLP workflows (BERT, topic modelling) for production environments. Agentic & Multi-Agent Systems: Design and orchestrate autonomous ...

Generative AI solutions in production environments. Demonstrable experience successfully designing and launching multiple enterprise GenAI implementations beyond proof-of-concept stage. Strong expertise in LLM ecosystems including OpenAI, Anthropic, Gemini, and related model platforms. Hands-on experience with RAG architectures, Agentic AI frameworks, and orchestration tools such as LangChain, LangGraph ...