9 of 9 Permanent Model Context Protocol Jobs in the City of London

serve. • Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG/context-graph retrieval, memory, tool/function calling, MCP integration and multi-agent orchestration. • Integrate to standards: connect agents into client ecosystems through proven integration patterns, standards-based APIs and secure authentication. • Make reusability … engineering bar, and grow the pod's agentic capability. Must Have Skills python LangGraph Open AI Agents SDK Model Context Protocol (MCP) Amazon Bedrock Large Language Models (LLMs ...

lead developer embedded within customer accounts, for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters. Build ...

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 ...

communities and economies globally for more than 300 years. Through a comprehensive suite of trusted financial market infrastructure services – and our open-access model – we provide the flexibility, stability and trust that enable our customers to pursue their ambitions with confidence and clarity. LSEG is headquartered in the United … enterprise-grade AI products and services. Build agentic AI workflows, retrieval-augmented generation (RAG) solutions and intelligent automation capabilities. Deliver scalable integrations with LLMs, MCP services, vector databases and enterprise platforms. Develop reusable AI frameworks, accelerators and engineering standards. Engineering Excellence Lead technical implementation across multiple AI initiatives. Define solution ...

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 ...

well. Willing to travel to customer sites one to two times per month. Desired Skills Kafka or event-streaming experience. Exposure to LLM Gateways, MCP, A2A, or agent identity/authorisation patterns. Scripting or development background (Java, Python). Startup or scale-up experience. Additional European languages are a plus. ...

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 ...

tool, built while the value case is made Delivering client POCs and MVPs: RAG pipelines, agent architectures, LLM integrations and protocol-driven tooling (MCP, tool orchestration) Iterating in short loops: demoing every few days, taking feedback, changing course without ceremony Engineering for production from day one: guardrails, security … data engineering to build the whole thing yourself Hands-on production LLM and agent experience: prompt engineering, agent workflows, RAG, tool orchestration and MCP, with evidence you've shipped AI users actually rely on, and how you proved it could be trusted Evaluation-driven habits: golden datasets, eval frameworks, guardrails ...

batch patterns, data graph design, and activation strategies within D360.Map and integrate external agents from hyperscalers (e.g., Copilot, Gemini) into Agentforce via open standards (MCP, A2A); advise customers on how these systems collaborate within their enterprise architecture.Navigate ambiguous, high-stakes technical conversations with authority — knowing when to prescribe, when … engineering, agent lifecycle management, and hands-on prototype development.Hands-on proficiency in programming (e.g., Python, JavaScript, SQL) and data frameworks.Understanding of agent interoperability standards (MCP, A2A) and the broader external agent ecosystem.Experience operating in complex, matrixed enterprise environments across multiple EMEA markets.Exceptional communicator and storyteller — able to translate architectural complexity ...