51 to 75 of 134 Model Context Protocol Jobs in England

architectures Strong understanding of distributed systems, service‐to‐service communication, API Gateways, Kubernetes, cloud‐native architectures Hands‐on experience working with emerging agentic protocols (MCP, A2A) and interoperability patterns Proven ability to work cross‐functionally with engineering, security, product, and platform organizations Excellent communication and storytelling skills, with the ability ...

solutions, including tool use, workflow orchestration, routing, memory, and multi‐agent patterns Expertise in prompt engineering, structured outputs, and reliability patterns to optimise model performance Ability to implement evaluation frameworks for quality, grounding, task success, safety, latency, and cost Experience deploying AI solutions into production with focus on cost … evaluating multi‐agent AI solutions, agent orchestration patterns, RAG architectures, and enterprise integrations Knowledge of agent communication protocols and interoperability patterns, such as MCP and A2A Ability to balance innovation with practical delivery considerations, including risk, cost, security, and production readiness Confidence contributing to pre‐sales while also being able ...

backend architecture Event-driven systems Developer platforms Production AI infrastructure Experience with technologies such as OpenAI, Anthropic, LangGraph, LangChain, Kubernetes, Kafka, Vector Databases, RAG, MCP, Temporal or similar would be highly advantageous. Ideal backgrounds Current or previous: Chief Technology Officer Founding CTO Technical Co-Founder VP Engineering Head of Engineering ...

tracing. Responsibilities: Serve as the lead developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable Return on Investment (ROI). Architect and code the connective tissue between Google s AI products and customer's live infrastructure ...

guide and review AI-generated code with confidence. Hands-on experience building agentic systems using frameworks such as LangGraph, Microsoft Agentic Framework, or MCP, with a solid understanding of LLM capabilities and risks. Experience designing and running experiments to validate ideas quickly, using technical prototypes, customer feedback, and lean startup ...

call rota as required Experience with LLM’s including realtime models Experience with Docker, Container Apps, containerised distributed systems, Terraform, Kafka, New Relic, MCP, Websockets Experienced in writing automated tests and maintaining CI/CD pipelines Proven experience of source and version control tools such as GitHub Be experienced working ...

deliver outcomes and build long‐term technical trust. Our Tech Stack Typescript, NodeJS, OpenAI, Microsoft Azure, Kubernetes, Docker, Terraform, MongoDB, MySQL, Redis, Kafka, MCP Who you are Experience level: 8+ years in software engineering, systems integration, DevOps, or data engineering roles with meaningful production responsibility. People leadership: proven ability ...

/UDP , packet and latency path analysis, dedicated network infrastructure, and kernel bypass techniques ( DPDK ) Familiarity with agentic engineering tools and concepts: LLMs , MCP APIs , AI‐First IDEs (e.g. Cursor, Claude Code), harness engineering , and workflow automation Ability to make sound architectural decisions, articulate trade‐offs clearly, and guide implementation ...

/ML solutions, ensuring security, scalability, and observability. Implementing CI/CD for ML services, writing infrastructure as code, and monitoring for model/data drift and performance. Establishing robust guardrails for safe AI usage, including prompt security, practical evaluation frameworks, and compliance with privacy regulations. Contributing to reusable … batch and real‐time). Familiarity with RAG architectures, prompt engineering, guardrails and evaluation techniques. Previous experience developing agentic capabilities, such as agent skills, MCP servers, tool usage. Strong grasp of security, privacy and governance principles (IAM, secrets, PII handling). Effective communication skills and ability to work with both ...

should have: Good understanding of Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), and modern AI frameworks such as LangChain, LangGraph, MCP, with experience developing AI-powered applications. Experience working with vector databases, semantic search, and retrieval optimisation techniques, along with an understanding of context management. Experience ...

business decisions. Build, tune, andoptimisemachine learning, deep learning, and generative AI models,leveragingboth established andcutting-edgetechniques. Designand buildLLM-powered systems— RAG pipelines, prompt and context engineering, fine-tuning, structured outputs, function calling, context-window management,and secure model integration … selectingappropriately between standard and reasoning (test-time-compute) models, balancing capability against latency and cost. Designand buildagentic AI systems— tool-calling architectures, interoperability protocols (MCP, agent-to-agent), multi-agent orchestration, and multi-step reasoning with human-in-the-loop andappropriate trust, safety, and security boundaries. Leverageand contributeto agentic engineering ...

Azure. Experience with Python/Fast API for backend development. Experience using AI-assisted development tools. Awareness or exposure to Model Context Protocol. Experience working with Figma or collaborating from design files. Exposure to AI agents or agent-based workflows. Benefits: Competitive salary based on experience. Opportunity ...

Group in London is seeking a Senior ML Engineer to advance safe, scalable agentic AI capabilities across the enterprise. You will design and operate MCP-based platforms and collaborate with product teams to deliver reliable AI tooling. The role requires strong Python, expertise with LLM frameworks, FastAPI, and Kubernetes, plus ...

record of production delivery Able to communicate ideas clearly and build strong relationships globally within the front office Previous experience of agentic design (harnesses), MCP servers, and related AI/ML python libraries Strong analytical, problem-solving, and architectural design skills, with an ability to operate effectively in a front ...

oriented programming language (e.g., JavaScript/TypeScript, Python, Java, C#, etc.). AI & Next-Gen Tech: * Hands-on experience with GenAI (e.g., Playwright with MCP). Required experience with AI-assisted coding tools (e.g., GitHub Copilot, Claude). Knowledge of LangChain , LangGraph , or cloud environments is highly desirable (a plus ...

others. Agentic AI & LLM Engineering Design and build production‐grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM‐powered systems; current knowledge of the latest model capabilities … product stacks. Machine Learning Engineering End‐to‐end ML lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery ...

others: Agentic AI & LLM Engineering: Design and build production-grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM-powered systems; current knowledge of the latest model capabilities … product stacks. Machine Learning Engineering: End-to-end ML lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Platform & Cloud Engineering ...

Job Description Purpose of the role: To develop, implement and maintain solutions that support the safeguarding of the bank’s systems and sensitive information. Accountabilities: Provision of subject matter expertise on security systems and engineering ...

agents across the business. You will own delivery, scale the function, and define its future. You\'ll work with Claude, Claude Code, MCP, LangChain to implement multi-agent systems, RAG pipelines, and tooling. This is a hands-on leadership role with three engineers initially. The role is fully remote within ...

tasks (containers, cloud configuration) — at Encore, engineers regularly jump between the two Experience working with AI-assisted coding tools, such as Claude Code Skills, MCP servers and Routines. A product and commercial mindset: the ability to weigh technical work against business impact, prioritise ruthlessly, and know what ...

others: Agentic AI & LLM Engineering Design and build production-grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM-powered systems; current knowledge of the latest model capabilities … product stacks. Machine Learning Engineering End-to-end ML lifecycleexpertise: feature engineering, model training,evaluationand production deployment, includingMLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn,PyTorch,XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Platform & Cloud Engineering Architecture and delivery ...

Amazon Bedrock and SageMaker Claude/Anthropic Azure AI Foundry or Azure OpenAI Databricks Vector databases and RAG architectures Agentic AI orchestration frameworks and MCP servers Model and data lifecycle management Desirable Experience Experience working within public sector or regulated environments Experience with secure government delivery environments Experience contributing ...

design and delivery of Generative AI solutions for clients. You will architect end-to-end AI systems, including LLMs, RAG, multi-agent workflows, and MCP-based integrations, collaborating with cross‐functional teams to meet defined metrics. The role requires hands-on expertise with modern AI foundations, tooling, and cloud platforms ...

without flinching. You run the incident, write the postmortem, fix the systemic cause, and move on. You're fluent with AI tooling. LLM APIs, MCP servers, and agentic frameworks, and you drive Claude Code, Cursor, or similar every day. You've shipped production network tooling or automation that other teams ...

pipelines and semantic search using vector databases such as Pinecone and Chroma, including indexing, retrieval policies, and evaluation. Build and integrate agent tools (MCP) and APIs to connect agents with external services, databases, and internal systems, ensuring robust output parsing, error handling, and retries. Design and implement robust evaluation frameworks … systematically assess and measure the performance of AI agents across key operational metrics. Practice advanced prompt and context engineering (e.g., Chain-of-Thought, ReAct, function calling/tool-use prompts), implement output validation and guardrails to reduce hallucinations. Deploy scalable AI services to cloud infrastructure, ensuring monitoring, and observability ...