1 to 25 of 139 Permanent Model Context Protocol Jobs in the UK

frontend and backend services—from designing highly performant, intuitive modern user interfaces to building robust API services, Model Context Protocol (MCP) services, and autonomous AI agents. In this role, you will lead the architecture, design, and deployment of scalable Generative AI solutions, ensuring seamless integration into … both frontend and backend development, combined with hands‐on experience building production‐ready GenAI features. You will bridge the gap between backend APIs, custom MCP services, agentic workflows, and responsive modern user interfaces to deliver intelligent, automated planning experiences for our global customers. Your Impact Technical Leadership & System Architecture: Architect ...

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 ...

systems on Amazon Bedrock, including multi‐agent orchestration, memory management, and tool integration using Amazon Bedrock AgentCore and Strands Agents. Build, integrate, and maintain MCP (Model Context Protocol) servers that expose capabilities to AI agents across client platforms. Architect and implement RAG pipelines using Amazon Bedrock Knowledge … environments, not limited to basic API calls. Agentic AI and multi‐agent systems – direct experience designing and deploying agent pipelines with real orchestration complexity. MCP (Model Context Protocol) – built or integrated MCP servers in a production or near‐production context. Strands Agents – familiarity with the framework ...

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 ...

workflows using approved AI platforms Develop API-driven integrations across AI, Microsoft, and enterprise systems Configure and manage Model Context Protocol (MCP) server implementations Collaborate with business leaders to identify and deliver automation opportunities Manage AI solution delivery from intake through deployment and support Ensure governance, security … workflows in production environments Background in legal technology, professional services, or another compliance-sensitive environment Working knowledge of Model Context Protocol (MCP) server configuration and deployment Familiarity with ethical walls, conflict management systems, and matter-based security models Experience integrating large language model APIs and deploying ...

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/ ...

Senior Lead Software Engineer at JPMorganChase within the Corporate Technology Data and Analytics Services team, you will be a founding contributor to the Context Plane — a greenfield platform that connects the firm's data mesh and knowledge sources to AI agents and large language model tools. You will … knowledge into a knowledge graph and vector store Build and evolve the serving layer — including graph and vector retrieval, GraphRAG, response assembly, and a Model Context Protocol endpoint consumed by downstream agents Extract and promote reusable components into a shared core library, reducing duplication across the platform ...

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 ...

solves it. Architecture within your domain, implementation, evaluation, and production operation. You are responsible for it working, not just for your code compiling. Design context and retrieval strategies. Decide what goes into the context window and what stays out. Build retrieval pipelines, conversation memory, and summarisation logic that … founding or early-stage engineer at a startup Experience with multi-agent coordination: task decomposition, handoff, and shared state Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards Published thinking or external contributions in AI engineering — talks, writing, open source Benefits Time ...

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 ...

hands-on building role, not an advisory position. What You'll Do Own the AI agent architecture. Design the orchestration layer, memory and context management, evaluation framework, and integration APIs that all agent products build on. Your decisions are the ones others build on top of. Ship production agents. … domains where AI output quality has compliance implications Published thinking or external contributions in AI engineering - talks, writing, open source Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards Benefits Time off - 27 days holiday, plus 5 additional days off: 1 life event ...

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/ ...

Systems RAG Retrieval Augmented Generation Prompt Engineering AI Evaluation AI Observability AI Governance Responsible AI AI Security Guardrails Model Context Protocol (MCP) Function Calling Tool Use AI Orchestration AI Platforms Experience with one or more: OpenAI Azure OpenAI Anthropic Claude Google Gemini AWS Bedrock Hugging Face LangChain ...

Integrate Azure AI services (Azure OpenAI, Cognitive Services, Azure ML, AI Search) into production applications. Architect and build LLM-powered features, agentic workflows, and MCP (Model Context Protocol) integrations to a production standard. Work directly with clients to understand requirements, shape solutions, and translate them into working … build client confidence. Comfortable working autonomously and shaping ambiguity into repeatable, scalable delivery. Advantageous Azure AI Engineer certification or equivalent practical experience. Understanding of MCP, vector databases, and evaluation/testing approaches for AI systems. Experience mentoring or leading small development teams. Why BCN? The opportunity to shape your ...

enterprise AI platform that powers AI products across the company. 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 … experience. LLMs & Agentic AI Hands‐on experience building applications powered by Large Language Models (LLMs). Experience building Model Context Protocol (MCP) and Retrieval‐Augmented Generation (RAG) solutions, making use of embeddings, vector databases, and modern LLM orchestration frameworks. Experience building or contributing to agentic AI systems ...

platforms such as Databricks or SageMaker, and familiarity with hybrid cloud strategies (Azure, on-prem Kubernetes). • Strong understanding of AI infrastructure for scalable model serving, distributed training, and GPU orchestration. • Expertise in Large Language Models (LLMs) and Small Language Models (SLMs), including fine-tuning and deployment for enterprise … libraries and tools for model training, evaluation, and deployment. • Knowledge of agentic frameworks (e.g., LangChain, AutoGen) and Model Context Protocol (MCP) for building autonomous AI workflows and interoperability. • Awareness of emerging trends in GenAI platforms, open-source MLOps, and cloud-native AI solutions ...

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 ...