101 to 125 of 194 Model Context Protocol Jobs in the UK

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 ...

individual-contributor role for engineers who live at the current edge of AI development and work fluently with Claude Code, Cursor, agents, eval harnesses, MCP, and modern TypeScript and Python. What You’ll Do Problem/Opportunity Discovery Sit with a business or clinical leader and reframe an idea … your own orchestration layer. Integrate frontier and self-hosted LLMs (Claude, GPT, Gemini, open-weight models) with tools, data, and external systems through MCP and custom connectors. Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with Elasticsearch or Solr, and BM25 + similarity ...

client delivery teams to design, build, and operate multi-agent AI systems on AWS. You'll own end-to-end implementations using Amazon Bedrock, MCP servers, and RAG pipelines, while ensuring observability, scalability, and secure integration with client applications. Hybrid and remote options offered, with offices in multiple cities. #J ...

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 ...

frameworks using network documentation, alarms, topology, inventory, KPIs, trouble tickets, procedures, configuration data, and operational knowledge. Develop multi-agent workflows using LangChain, LangGraph, MCP, or similar frameworks. Implement vector search, semantic retrieval, graph-enhanced retrieval, and hybrid search patterns. Develop AI agents for fault diagnosis, root-cause analysis, KPI analysis … systems. Strong Python programming skills. Experience with ML fundamentals, deep learning concepts, embeddings, transformers, and LLM architectures. Experience using LangChain, LangGraph, LlamaIndex, AutoGen, MCP, or similar AI frameworks. Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, ChromaDB, or equivalent. Experience with graph databases, knowledge graphs, or Graph APIs. ...

real lifecycle work: intake triage, estimation, requirements, technical design, coding, testing, release, and operations. Engineer the scaffolding that makes agents dependable tool use via MCP, agent-to-agent handoffs (A2A), event-driven orchestration, and deep Jira and enterprise system integration. Build on Equinix's enterprise AI platform AI gateway, orchestration … when to demand human review, and the communication skills to explain that reasoning. Preferred Qualifications Experience with agent frameworks and protocols such as MCP, A2A, Anthropic or OpenAI APIs, Bedrock, Vertex, orLangGraph. Experience building developer platforms, orchestration systems, or SDLC tooling, including Jira, GitHub, or ServiceNow integration. Knowledge-engineering experience ...

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 ...

React: the agents people talk to and the apps they click through, built in TypeScript. And you don’t stop there. From API and MCP design to infrastructure on Vercel, AWS, Supabase, and BigQuery, plus the agents that work across the stack, you own complete features end to end, including ...

React: the agents people talk to and the apps they click through, built in TypeScript. And you don't stop there. From API and MCP design to infrastructure on Vercel, AWS, Supabase, and BigQuery, plus the agents that work across the stack, you own complete features end to end, including ...

RESTful APIs, SQL, and some exposure to mobile development Familiarity with AI-assisted development tools (e.g. Codex, Claude Code) and emerging patterns such as MCP Strong domain knowledge in marketing automation, CDPs, eCommerce systems, analytics, or data engineering Advanced discovery and requirements-gathering skills across multi-layered stakeholder environments Executive ...

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 ...

three pillars of the AI strategy; AI for Service Delivery, AI forDigital Products and AI for Productivity. This spans the JTC Agentic Platform(MCP-enabled orchestration and validation across JTC systems and market tools),the enterprise document extraction platform, end-to-end billing agents (AgenticThames and the Agentic Wheel), conversational … institutionalise the multi-platform Agent Builder for craft-your-own agents. Agentic Platform & AI Architecture Own the engineering of the JTC Agentic Platform, MCP-enabled orchestration and validation across JTC systems and market tools, maintaining in-house control of the agentic layer to avoid LLM lock-in. Build around ...

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 via APIs and data pipelines, with strong security, data governance ...

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 via APIs and data pipelines, with strong security, data governance ...

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 via APIs and data pipelines, with strong security, data governance ...

RESTful APIs, SQL, and some exposure to mobile development Familiarity with AI‐assisted development tools (e.g. Codex, Claude Code) and emerging patterns such as MCP Strong domain knowledge in marketing automation, CDPs, eCommerce systems, analytics, or data engineering Advanced discovery and requirements‐gathering skills across multi‐layered stakeholder environments Executive ...

applications inside client systems, ensuring solutions meet enterprise requirements for performance, security, and reliability. Develop technical assets - Create reusable, production‐ready artefacts including MCP servers, sub‐agents, agent orchestration frameworks, and evaluation tooling that can be deployed across client workflows. Support enterprise deployments - Play a key technical role in rollouts … Identify patterns and scale - Recognise repeatable deployment patterns across engagements and contribute those insights back to Accenture's I&E FDE practice and our model provider partners to continuously improve our delivery playbook. Maintain cutting‐edge expertise - Stay ahead of the curve on LLM capabilities, agentic frameworks, evaluation methodologies ...

systems in production or near‐production settings. Experience with generative AI systems, LLM‐enabled applications or evaluation pipelines. Experience with cloud platforms, MLOps tooling, model serving or platform engineering. Advanced use of Linux, Docker, version control, CI/CD and service‐oriented deployment practices. Experience working across the lifecycle … government or academia. Experience in regulated industries such as insurance, financial services, public sector or national‐scale infrastructure contexts. Familiarity with agentic systems design, MCP/A2A protocols, or inference‐time privacy controls. Familiarity innovating with agentic‐driven workflows and toolsets for both research and prototyping tasks, such as Claude ...

product requirements, and to provide early, high-level advice on opportunities and possible solutions Stay current with emerging AI trends, technologies, protocols, research breakthroughs, model development and AI software design pattern and best practices Provide technical guidance and mentorship to the engineering teams. Collaboration and Communication: Foster strong relationships … focus in AI/ML domains. Proven experience designing and deploying AI solutions using frameworks such as LangChain, RAG, and vector databases or the MCP protocol. Deep understanding of LLMs, prompt engineering, model evaluation, and fine-tuning strategies. Strong proficiency in ML frameworks (e.g., TensorFlow, PyTorch) and cloud platforms ...

SDLC Exposure to AI and agentic systems (practical or conceptual), including: Security and governance for AI Solutions (Identity, observability, risks) Agentic Architecture and patterns (MCP, RAG, Tools, Memory, Harnesses) Experience working in complex, enterprise‐scale environments Ability to translate security requirements into engineering‐aligned solutions Hands‐on experience building, testing ...

behind them. Essential Experience Building and deploying production LLM applications Strong experience with RAG including: Document ingestion Chunking strategies Embeddings Vector search and retrieval Context engineering Prompt orchestration LLM deployment experience, not just experimentation Azure OpenAI, Ollama and/or Vertex AI Python, FastAPI and REST API development LangChain … evaluate and improve model performance How to expose AI capabilities through APIs and enterprise applications Desirable Azure AI Foundry Agentic AI MCP Private LLM deployments Enterprise security and regulated environments Not Suitable If Experience is primarily notebooks, research or experimentation No production LLM deployment experience No RAG implementation experience ...

OpenAI, open-source models via vLLM) Own database performance for both relational (Postgres) and vector (pgvector/Pinecone) workloads Collaborate with ML engineers on model-serving infrastructure and prompt-caching strategies Drive API versioning and backwards compatibility as the product iterates quickly Core Tech Stack: Languages: Python (FastAPI … security, and scalability best practices Nice to Have: Experience fine-tuning or evaluating open-source models Familiarity with agentic frameworks (function calling, tool use, MCP) Prior startup/scale-up experience Benefits: 25 days holiday + bank holidays Private healthcare (Vitality) £1,000 annual learning budget Hybrid working with full ...

that automate insurance workflows end-to-end across Policy, Billing, and Claims domains. The role owns the patterns, frameworks, and standards for agent orchestration, MCP-based tool ecosystems, agent memory, planning, evaluation, and safety. RAG and conversational features are table stakes; the forward agenda is autonomous and semi-autonomous agents … pricing intelligence, billing troubleshooting, and beyond - across our platform and technology stacks. Key Responsibilities Own the architecture of EIS's agentic platform: agent orchestration, MCP-native tool ecosystems, agent memory (short-term, long-term, semantic), planning, and tool/function calling patterns reusable across product domains. Enable and provide support ...

small, high-leverage team building production infrastructure for Generative AI at the company and DoorDash, with a primary focus on our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll work across model … routing, vendor abstraction, or cost attribution Experience building developer platforms, internal platforms, or self‐serve infrastructure Experience building and deploying AI agents or MCP servers in production Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases Diversity, Equity and Inclusion At the company, we know that ...