51 to 75 of 111 Model Context Protocol Jobs in London

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

investment discussions. Reduce manual effort for Relationship Managers while improving the quality and consistency of client recommendations. Skills Solution architecture Architecture AI LLM MCP Gateway Job Title: AI Solution Architect Location: London, UK Rate/Salary: 500.00 - 550.00 GBP Daily Job Type: Contract Trading as TEKsystems. Allegis Group Limited, Maxis ...

design, product mechanics, and client requirements, translating business needs into scalable platforms. Apply modern AI and Generative AI techniques — including RAG, agent architectures, and context engineering — to enhance structuring workflows such as trade idea generation, documentation, and client engagement Drive the design and development of production-grade applications with … Experience with quantitative modelling and derivatives pricing Hands-on experience with AI/ML frameworks and tooling such as LangChain, LangGraph, vector databases, and MCP-based orchestration Expertise in relational and/or NoSQL databases, data pipelines, and ETL processes Experience with microservices architecture and web development Knowledge of data ...

verbal communication skills with the ability to influence technical and business stakeholders. Preferred Qualifications Experience with agent frameworks, orchestration platforms, and protocols such as MCP, A2A, LangGraph, Bedrock, Vertex AI, OpenAI, Anthropic, or similar technologies. Experience building developer platforms, orchestration engines, SDLC tooling, or engineering productivity solutions. Experience implementing enterprise … systems within regulated or security-conscious environments. Experience with responsible AI governance, evaluation frameworks, and model quality practices. Contributions to open-source software, technical publications, patents, or industry conference presentations. the company Values At the company, we operate with a growth mindset and embrace diversity of thought and contribution. ...

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 ...

fast‐paced environments. Experience building benchmarks and datasets to train/evaluate AI systems or agents in production settings. Experience with AI infrastructure (model deployment, optimisation, data … processing) and cloud platforms (AWS, Azure or GCP). Hands‐on experience with a range of AI concepts such as LLMs, VLMs, RAG, agents, MCP, computer vision, GraphML, recommendation systems or reinforcement learning. Familiarity with ML frameworks (PyTorch, TensorFlow, etc.) and strong proficiency in Python and data structures and algorithms. ...

used by institutional clients. As an early member of a small, senior team, you will work end‐to‐end across research, prototyping, data engineering, model implementation, production systems, and client‐facing applications. This is a high‐ownership role for someone who is comfortable contributing wherever needed in a fast … help harden new modelling approaches, analytical tools, and platform features. Work across the design and build of our internal AI layer, including LLM integrations, MCP servers, and agentic tooling, to automate research workflows and deliver analytical insights within the client‐facing platform. Help with the evolution of our cloud infrastructure ...

maintaining human-in-the-loop systems and AI monitoring/observability Strong fundamentals in AI/ML evaluation frameworks Experience building agentic systems including MCP, tool calling, memory systems, vector-based knowledge stores, guardrails In depth understanding of modern software design principles (e,g. microservices, event-driven architectures, domain driven … Management is a plus Key Responsibilities Develop and maintain Python-based AI applications Build and maintain document workflows using LLMs and classical NLP Debug model performance issues, handle edge cases, and optimize system reliability Write comprehensive tests and implement monitoring for AI/ML systems Rapidly prototype new features ...