251 to 275 of 361 Model Context Protocol Jobs in England

functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working across the stack, including modern … code. That experience could be commercial, independent or through substantial personal projects what matters is that you can explain what you built, how the model was integrated and the engineering decisions surrounding it. Why consider it? This is an opportunity to join a substantial software organisation at a point ...

functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working across the stack, including modern … code. That experience could be commercial, independent or through substantial personal projects what matters is that you can explain what you built, how the model was integrated and the engineering decisions surrounding it. Why consider it? This is an opportunity to join a substantial software organisation at a point ...

solve real, complex business problems. Owning delivery end-to-end - from initial exploration through to production deployment, integration and ongoing iteration. Working with MCP-style architectures (FastMCP/FastAPI) and LangChain/LangGraph or equivalent frameworks to build reliable, scalable systems. Integrating AI into products, APIs and core business workflows … principles. Cloud deployment experience across AWS, GCP or Azure, using modern engineering practices. A product mindset - you care about impact and adoption, not just model performance. You understand LLM and machine learning trade-offs and design systems accordingly. About you: You'll likely have a Masters or higher ...

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 Example projects you might ...

empower specialist teams across the organization. Understand Agent Interoperability - Map and integrate external agents from hyperscalers (e.g. Copilot, Gemini) into Agentforce via open standards (MCP, A2A); design how these systems collaborate. Enable Conversational & Background Agents - Use Agentforce Studio and Agent Builder to configure chat and background agents; integrate with external … Agentforce and deep fluency with D360 and core Salesforce platform services. Advanced Integration - Experience integrating Salesforce with external agents via APIs and open standards (MCP, A2A). Governance & Observability - Familiarity with prompt governance, observability, and monitoring frameworks. Cross‐Platform Background - Background in cross‐platform integrations (e.g. Hyperscaler SDKs to Salesforce ...

controls in place so AI outputs remain reliable and cost‐effective. Scale practical AI adoption Design and implement AI workflows as skills, models and MCP tools in OpenWebUI, with cost control, provider routing and observability built in. Partner with other departments to turn AI capabilities into everyday workflows, reusable tools … build durable institutional capability. Reliability, cost control, observability, instruction‐following, usability and adoption matter most. The team designs reusable workflows, skills, models and MCP tools that support Penta’s move towards an AI‐native advisory operating system. Keeping Penta at the forefront of practical AI in PR, communications and strategic ...

agents in the loop on every change. That's leverage, and it's a new attack surface: prompt injection against agent harnesses, untrusted MCP server outputs, IAM scope creep on agent-driven tooling, model and prompt supply chain. You'll own the security layer of our agent platform: sandbox … paths, KMS misuse, and the defences for each Incident response leadership end-to-end on a real incident (containment, eradication, forensics, write-up) Authoring MCP servers or custom agent tools with a security lens Product Security Engineer Job Description 3 Familiarity with AGENTS.md/CLAUDE.md patterns and skill authoring Cryptography ...

subject matter specialists across areas such as agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, and model platforms and inference providing the architectural vision, technical governance, and cross-domain coherence that binds their contributions into a unified, enterprise-ready system. You will …/IAP binding, and defense-in-depth enforcement Define the layered guardrail framework applied at every boundary, balancing protection with performance Govern the MCP control plane registry, gateway, and risk scoring across all internal and third-party servers Mandate adopt-over-build for productized evaluation and observability stacks Establish FinOps ...

will build and operate the runtime, governance, and developer experience that product and service teams use, from model routing, caching, shared RAG services, MCP interfaces, Spec Kits, AI frameworks, Skills registries, to prompt management. You ensure AI agents and workloads run in a responsible (AI FinOps) and technically robust … teams with AI FinOps, quotas, usage metering, and safety/compliance guardrails built in. Define and maintain platform standards (frameworks, SDKs, Spec Kits, MCP interfaces), curating the AI tool landscape and managing lifecycles from sandbox to standardization or retirement. Implement platform-wide observability (metrics, logs, traces), SLOs, cost and performance ...

effectively. Задачи: Design, build, and operate core GenAI platform components, including an LLM routing gateway, vector search and RAG infrastructure, tool registry and MCP gateway, AI observability and evaluation tooling, and infrastructure for long-running agentic workflows; Own production-quality delivery of platform features from design through rollout, monitoring … asynchronous workflows, idempotency, consistency trade-offs, and designing for failure; Hands-on experience with LLM APIs or strong interest in learning about rate limits, context windows, multi-vendor routing, latency variance, and cost control; Security mindset for AI systems, including prompt injection risks, PII in logs, data leakage ...

automation Hands-on experience with Claude Code (or equivalent agentic CLI tooling — Cursor, Codex CLI, Cline, Aider): writing custom skills, building subagent systems, implementing MCP servers, and authoring AGENTS.md/CLAUDE.md context files Platform-as-a-product mindset: builds reusable assets with their consumers in mind, prioritises discoverability … infrastructure patterns that form the core of the central platform’s offering Develop Claude Code (or equivalent agentic CLI tooling) skills, subagents, and MCP servers that become the standard AI tooling distributed to all client teams Implement golden path templates — project scaffolding, pipeline configuration, security guardrails, AI-native workflow defaults ...

Experience architecting the data layer behind AI or analytics products in production. Informed views on the trade‐offs between fine‐tuning, RAG, and long‐context approaches, and how each changes data architecture … requirements, including context window economics and prompt caching. Familiarity with infrastructure as code, CI/CD, and observability for data platforms. Awareness of MCP and tool‐orchestration concepts and their implications for composable, data‐driven AI systems. Fluent use of AI‐assisted development tools (e.g. Claude Code, Cursor), with ...

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 ...

experience with AI Development Life Cycle practices and methodology. Orchestration : Production, hands‐on experience with an agentic framework such as LangGraph, plus familiarity with MCP‐based tool orchestration. Exposure to AutoGen, LlamaIndex, or CrewAI is a plus. Production Python engineering : Clean, modular, testable, maintainable code — you care about system reliability … much as model output. Evaluation & observability : Practical experience instrumenting tracing (LangSmith, Arize) and building CI/CD pipelines built specifically for LLMs. Engineering foundations : Distributed systems, scalable data pipelines (Kafka, SQL), and cloud‐native infrastructure. Cloud‐native deployment: Deep familiarity with shipping agentic workloads into production environments, ideally leveraging ...

Hands-on experience building AI/ML applications (RAG, agent frameworks, MCPs, skills, harnesses) and taking them to production or in platforming (LLM or MCP gateway, agentic runtime, auth, data retrieval, eval tooling) Experience running AI systems in production at scale, including observability, cost and capacity planning, regression detection … Hands-on experience building AI/ML applications (RAG, agent frameworks, MCPs, skills, harnesses) and taking them to production or in platforming (LLM or MCP gateway, agentic runtime, auth, data retrieval, eval tooling) Experience running AI systems in production at scale, including observability, cost and capacity planning, regression detection ...

with the modern LLM/agent toolkit — model APIs, prompting, tool use, RAG, and the patterns this fast‐moving ecosystem is converging on (MCP, agent frameworks, evals). You enjoy working closely with non‐engineers — you are the kind of person who'll sit with a scientist to understand … sustainable work-life balance and we are open to discussing flexible working arrangements. We are a hybrid team operating on a highly collaborative model that values synchronization and pair programming. You will be welcomed by a team of colleagues with decades of accumulated experience in their areas of expertise ...

review steadily upward. Pipeline Engineering Build and maintain the agentic pipeline: agent harness, blueprint engine interleaving deterministic steps (lint, build, git) with AI reasoning, MCP tool layer exposing internal APIs, warm sandbox pool, and retrieval over the codebase, tickets, and historical PRs. Own the economics of the pipeline: observability over … agent-proposed solution meets performance, scalability, and reliability requirements, and challenging designs that will not hold up under real workloads. Experience building agent infrastructure: MCP servers and tool layers, sandboxed execution, and CI/CD adapted for agent-driven workflows. Deep enough .NET/C# and SQL reading fluency ...

review steadily upward. Pipeline Engineering Build and maintain the agentic pipeline: agent harness, blueprint engine interleaving deterministic steps (lint, build, git) with AI reasoning, MCP tool layer exposing internal APIs, warm sandbox pool, and retrieval over the codebase, tickets, and historical PRs. Own the economics of the pipeline: observability over … agent-proposed solution meets performance, scalability, and reliability requirements, and challenging designs that will not hold up under real workloads. Experience building agent infrastructure: MCP servers and tool layers, sandboxed execution, and CI/CD adapted for agent-driven workflows. Deep enough .NET/C# and SQL reading fluency ...

design decisions, and assess technical risk Deep expertise in enterprise AI applications built on large language models, including agents, retrieval augmented generation, orchestration frameworks, model optimization, and evaluation frameworks … Hands-on experience building AI/ML applications (RAG, agent frameworks, MCPs, skills, harnesses) and taking them to production or in platforming (LLM or MCP gateway, agentic runtime, auth, data retrieval, eval tooling) Experience running AI systems in production at scale, including observability, cost and capacity planning, regression detection ...

zero. Based in Oxford, you will work in a dynamic and growing engineering organisation that builds cloud‐based software to track and model power grids around the world. As a Senior Software Engineer on the AI team you will build systems where LLMs autonomously plan, and call tools … infrastructure Desirable attributes: Hands‐on experience building LLM‐based systems Experience testing non‐deterministic systems Understanding of statistics and inferential methods LangGraph, RAG, MCP, MLOps, IaaC, AWS What we offer Some of the benefits we include are: Private Medical Insurance Dental Insurance Parental Support Salary‐Exchange Pension Employee Assistance Programme ...

Serve as a 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, including APIs, legacy data … complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).Experience in a post-sales or technical consulting delivery function. Knowledge of Large Language Model (LLM) native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing. Strong communication skills, and proven experience enabling ...

scale their impact. We value your AI skills if have built fluency across the agentic engineering toolchain — coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agent frameworks. And you can describe projects where you shipped real work with these tools. You know how to drive … analysis, auto-triage of build/test failures).Establish policies and guardrails for AI usage in internal tools: data handling, prompt/response logging, model/provider selection, evaluation, fallback behaviors, and abuse prevention.Dev Productivity & Enterprise Tooling (Global Scale)Set technical direction and standards for the globally used developer ...

agentic workflows, from the first prototype through to something our clients depend on quarterly. Own the full path of a feature: data in, model or agent in the middle, deterministic validation around it, and an interface that makes the output reviewable by a human who has to sign … documentation nobody else wants to write. You go beyond prompt‐and‐paste. You’ve built or configured something (agentic workflows, evaluation harnesses, custom tooling, MCP integrations, automated review or triage) that made a repeatable part of engineering measurably faster or more reliable. You know where AI output cannot be trusted. ...

products, developer tools, coding assistants, agents, or LLM-based workflows. Strong understanding of how modern coding agents operate, including tool calling, context management, MCP, and evaluation techniques. A track record of driving technical direction in ambiguous and rapidly evolving areas. Strong product instincts and an ability to balance innovation ...

governance, administration, security, and enablement features that internal teams require to build and run AI agents and workloads at scale — spanning our LLM Gateway, MCP Gateway, agent runtime, and model governance controls. This role requires strong technical judgment, a deep understanding of enterprise deployment patterns, and the ability … elegant, self-service product experiences for builders across the firm. Key Responsibilities Own the roadmap for core platform capabilities including the LLM and tool (MCP) gateways, web search access, audit logging, access controls, and integration of approved AWS and Azure AI services. Develop and maintain the builder experience ...