1 to 25 of 111 Model Context Protocol Jobs in London

including LLMs, retrieval-augmented generation (RAG), autonomous and multi-agent systems, and modern interoperability standards such as the Model Context Protocol (MCP)—coupled with excellent communication skills to engage with clients and internal teams effectively. Primary Skill Set Generative AI Expertise : Good understanding of modern Generative … Development Kit (ADK). Experience building agentic workflows with memory, state management, and reliable multi‐step task execution. Model Context Protocol (MCP) & Interoperability : Practical understanding of MCP for standardized, secure connectivity between LLMs/agents and external tools, data sources, and systems. Ability to build and consume ...

Generative and Agentic AI, including LLMs, retrieval-augmented generation (RAG), machine learning, and interoperability standards such as the Model Context Protocol (MCP). The role involves shaping the Generative AI strategy, selecting suitable models and technologies, and collaborating with cross‐functional teams to deliver customer‐centric, enterprise … ability to architect reliable agentic workflows with memory, state management and safe multi‐step task execution at scale. Model Context Protocol (MCP) & Interoperability – architectural knowledge of MCP for secure connectivity between LLMs/agents and enterprise tools, data sources, and systems; ability to build and govern MCP ...

prompt management solutions. Configure and secure API gateways for internally developed AI services and third-party integrations, including Model Context Protocol (MCP) connectors. Provision and maintain sandbox and staging environments to support rapid AI development and experimentation. Work closely with Information Security and Compliance teams to ensure … solutions Serverless inference Containerised AI workloads Model versioning and evaluation pipelines Experience configuring and securing API gateways and AI integration services. Understanding of MCP (Model Context Protocol) and modern AI connector architectures. Strong Infrastructure-as-Code experience using Terraform. Experience working with CI/CD pipelines ...

just report it, including the greenfield Organiser and Exhibitor Agentic products our founding Agentic team is building on Model Context Protocol (MCP). Your Role Own the architecture of Grip's platform-wide data and AI infrastructure — from how event data is captured and modelled … batch and streaming — with reliability and low latency at the core Own the data layer that underpins Grip's Agentic products, ensuring agents and MCP-based tools have access to clean, well-modelled, real-time data Define data contracts and standards that product squads (Engage, Manage) build against, working ...

governance, and delivery support. CE AI owns and operates core AI platforms including LSEG AI Assist, the Question Answering Service (QAS), and the Internal MCP Gateway. Rather than delivering individual business use cases end‐to‐end, the team enables product engineering groups across LSEG to expose knowledge, data, and actions … agents in a consistent, governed, and repeatable way. The team operates a Central MCP Delivery model: building critical MCP tools and services “for” product teams where required, while simultaneously defining standards, patterns, and platform capabilities that allow teams to progressively move towards self‐service contribution. LSEG AI Assist/ ...

collaborative culture through communities of practice and emerging-technology events. You will explore and operationalize emerging patterns such as agent-to-agent communication, model context protocols, and agentic orchestration, turning early-stage concepts into scalable, production-ready capabilities. Job Responsibilities Design, develop, and troubleshoot software solutions using creative … programming and database querying languages Experience with containerization Knowledge of agent-to-agent (A2A) communication concepts Familiarity with Model Context Protocol (MCP) Experience with agentic orchestrators, personal AI assistants, or AI skills development J.P. Morgan is a global leader in financial services, providing strategic advice and products ...

experience designing and implementing multi‐agent architectures (task decomposition, coordination, orchestration, and agent‐to‐agent (A2A) interaction patterns) Model Context Protocol (MCP) & Integrations: Experience integrating agents with enterprise tools and data sources using MCP or equivalent context‐sharing patterns Knowledge Graphs & Reasoning: Building and leveraging knowledge … graphs for context enrichment, reasoning, and workflow automation RAG & Knowledge Systems: End‐to‐end RAG pipelines using enterprise search + vector stores (e.g., Elastic, Pinecone) with grounding, evaluation, and optimization Model Lifecycle & Governance: Model evaluation, monitoring, prompt/version control, and Responsible AI/MRM compliance Enterprise ...

rapid deployment of GenAI applications (including Retrieval-Augmented Generation (RAG) pipelines) and agents with planning, memory, and workflow orchestration Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management Develop systems to monitor model performance and address data and model drift Recommend best practices … Familiarity with advanced AI/ML concepts and protocols, including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP) Hands-on experience with agentic frameworks (LangChain, CrewAI, AutoGen, LangGraph, ADK) Strong communication skills for both technical and non-technical audiences Preferred qualifications, capabilities ...

rapid deployment of GenAI applications (including Retrieval-Augmented Generation (RAG) pipelines) and agents with planning, memory, and workflow orchestration Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management Develop systems to monitor model performance and address data and model drift Recommend best practices … Familiarity with advanced AI/ML concepts and protocols, including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP) Hands-on experience with agentic frameworks (LangChain, CrewAI, AutoGen, LangGraph, ADK). Strong communication skills for both technical and non-technical audiences. Preferred qualifications, capabilities ...

scalable, self-service solutions for documentation, SDKs, configurations, and pipelines to enable rapid deployment of GenAI applications and agents Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management Develop systems to monitor model performance and address data and model drift Recommend best practices … Familiarity with advanced AI/ML concepts and protocols, including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP) Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Previous experience deploying and managing ML models Experience working ...

improve SEO, conversion rates, and user experience. Build production AI solutions using Vercel's AI SDK and AI Cloud—including conversational interfaces, production agents, MCP servers, and agentic workflows that transform customer operations. Embed with strategic customers to build production applications—working within their systems, understanding their constraints, and shipping … Core Web Vitals, with experience diagnosing and fixing LCP, CLS, and INP issues at scale. Have experience with Model Context Protocol (MCP), building MCP servers, or implementing complex tool‐use patterns in production AI applications. Have led enterprise AI transformation initiatives, including building production agents that automated ...

architecture of the subscription lifecycle, including sign-up, trials, plan changes, upgrades, downgrades, proration, pauses, cancellations and win-back. Define and evolve the entitlements model that connects a customer’s billing status with the content and services they can access across web, mobile and connected television platforms. … Legal. AI-enabled engineering Apply our standards for using large language models and agentic tools across software design, development, testing and operations. Treat specifications, context, prompts, agent configurations and evaluation criteria as version-controlled engineering artefacts. Design and improve guardrails, evaluations and quality gates for AI-assisted development ...

infrastructure to accelerate AI solution development. Design and develop multi‐agent systems for orchestration, agent‐to‐agent communication, eval, memory, telemetry, and guardrails. Apply context and prompt engineering techniques to improve prompt‐based model performance. Develop and maintain tools and frameworks for prompt‐based agent evaluation, monitoring … with engineers, data scientists, and ML practitioners. Experience in customising and optimizing Github Copilot using VSCode Extension or Model Context Protocol (MCP). Experience with AWS and infrastructure‐as‐code tools such as Terraform. Experience in multi‐agent orchestration. Familiarity with MLOps practices, including CI/ ...

infrastructure to accelerate AI solution development. Design and develop multi-agent systems for orchestration, agent-to-agent communication, eval, memory, telemetry, and guardrails. Apply context and prompt engineering techniques to improve prompt-based model performance. Develop and maintain tools and frameworks for prompt-based agent evaluation, monitoring … with engineers, data scientists, and ML practitioners. Experience in customising and optimizing Github Copilot using VSCode Extension or Model Context Protocol (MCP) Experience with AWS and infrastructure‐as‐code tools such as Terraform. Experience in multi‐agent orchestration. Familiarity with MLOps practices, including CI/ ...

enterprise AI platform that powers AI products across 9fin. Develop scalable AI services supporting Agentic AI, Retrieval-Augmented Generation (RAG), embeddings, vector search, model serving, and intelligent automation. Build orchestration layers that transform structured and unstructured enterprise data into reusable AI capabilities. Develop production-ready AI applications using modern … Agentic AI Have hands-on experience building applications powered by Large Language Models (LLMs) . Have experience building Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) solutions, making use of embeddings, vector databases, and modern LLM orchestration frameworks. Have experience building or contributing to agentic ...

embeddings, and fine-tuning strategies Implement orchestration pipelines using Semantic Kernel and LangGraph for multi-agent workflows Integrate tools, APIs, and enterprise systems using MCP (Model Context Protocol) or similar frameworks Design memory, state management, and context-aware agent behaviors Optimize performance, cost, and latency … complex orchestration workflows Build scalable AI microservices Define AI governance patterns Troubleshoot production AI solutions Technologies Azure OpenAI Azure AI Foundry Semantic Kernel LangGraph MCP Azure AI Search Event-driven architectures ...

embeddings, and fine-tuning strategies Implement orchestration pipelines using Semantic Kernel and LangGraph for multi-agent workflows Integrate tools, APIs, and enterprise systems using MCP (Model Context Protocol) or similar frameworks Design memory, state management, and context-aware agent behaviors Optimize performance, cost, and latency ...

standardize AI agent development across teams. AIOps, Production Monitoring & Operations Establish monitoring, observability and operational governance for production AI workloads. Track agent performance, model latency, cost, prompt effectiveness, error rates and quality signals. Define alerting, incident response, RCA processes and production runbooks for AI applications. Troubleshoot AI agent issues … similar agent frameworks. Experience with Bedrock Knowledge Bases, vector databases such as OpenSearch, Pinecone or Weaviate, and embedding-based retrieval patterns. Experience with MCP (Model Context Protocol), enterprise tool integration and workflow automation. Knowledge of AI safety, guardrails, governance, responsible AI and GenAI FinOps. Experience integrating ...

support. Smartsheet is building a market-leading AI narrative anchored in organizational intelligence, agentic workflows, and the Model Context Protocol (MCP), and this role exists to make that narrative land with EMEA field teams and the enterprise buyers they serve. As a Sr. Product Marketing Manager … executives and solution engineers. Build and maintain the EMEA AI content suite in Smarsheet’s enablement platformHighspot: discovery slides, AI use‐case messaging guides, MCP value narratives, and competitive positioning against regional alternatives. Track enablement adoption through leading indicators including Gong keyword usage, Highspot engagement, and qualitative field feedback ...

data delivery platform. As part of the API team, you will build the next generation of Meltwater’s AI and platform capabilities, focused on MCP (Model Context Protocol), OAuth, permissions, and enterprise-grade governance. Your work will help bring Meltwater’s intelligence into external AI workflows. What … design, development, and optimization of API, export and MCP features. Collaborate closely with product managers and senior engineers to define functionality, while architecting systems that handle high-throughput data pipelines. Participate in building robust export pipelines, streaming architectures, webhook integrations and MCP servers. Maintain high observability and reliability standards using ...

differentiated customer experiences. A key part of the role is evaluating and integrating emerging AI capabilities — including LLM-powered features, AI agent frameworks, and MCP servers — to accelerate both internal tooling and customer touchpoints. You'll join an existing Senior Solutions Architect and share technical ownership of theplatform, dividing focus … platform architecture, and presenting technicalrecommendations to the team. This increasingly includes AI-powered capabilities: evaluating LLM APIs (such as Anthropic Claude or OpenAI), building MCP servers toexpose internal data to AI agents, and prototyping agentic workflows that automaterepetitive commercial processes. You'll investigate and resolve production bugs,optimize web portal ...

where ceremony adds cost without value. The role engages the Chief Architect early, pushes back constructively when platform standards do not fit the ECS context, and helps teams close decisions with conviction rather than endless review. Reason About Cost and Unit EconomicsInfrastructure choices are sized, cost trajectories modelled … signal back into product and architecture decisions Bonus Points Background in email security, threat detection, DLP, identity, or data platforms Familiarity with agentic AI, MCP (Model Context Protocol), or LLM application architecture Experience with eval design for AI features, product discovery practices, or data contract/data ...

architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance. Experience in designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust. Ability to design end‐to‐end secure, observable multi‐agent systems using complex design patterns (ReAct … expertise, systems engineering, and coding skills to build an engineering squad. Identify skill gaps in emerging tech (Model Context Protocol (MCP), tool‐calling, and foundation models), ensuring the team maintains subject matter expertise in an evolving AI stack. Collaborate with product and engineering to resolve blockers ...

Conversational AI and CX applications, transitioning from rapid prototypes to production‐grade agentic workflows (e.g., multi‐agent systems, Model Context Protocol (MCP) servers) that drive measurable return on investment. Architect and code conversational flows that are not just functional, but optimized for the "connective tissue" between ...

Conversational AI and CX applications, transitioning from rapid prototypes to production‐grade agentic workflows (e.g., multi‐agent systems, Model Context Protocol (MCP) servers) that drive measurable return on investment. Architect and code conversational flows that are not just functional, but optimized for the "connective tissue" between Google ...