176 to 200 of 361 Model Context Protocol Jobs in England

team and set the bar in design reviews and code reviews. Engage with the open source and agentic development community around sandboxing, MCP, and related ecosystems. Participate in on-call rotations and respond to critical stability issues affecting users. Document your work clearly and share knowledge with the rest ...

DeveloperHas breadth of experience across different tech-stacks, such as JavaScript/TypeScript, Java, C#, or Python, amongst othersExperienced in RAG Weaviate, OpenAI, Agents, MCP, Langchain, Prompt Engineering, Browser-Use, and DjangoExperienced building AI Tools, Agents and Agentic workflows (LangGraph, CrwAI)Hands-on Product building experience that cuts across … process Understanding of non-functional requirements and testing, with a focus on performance (but also accessibility, compatibility and aspects of security) Nice to have MCP, Django and nice-to-have Entra/GunicornGitHub Actions, GitLab pipelines, Azure DevOps pipelines, Jenkins (any experience on these will be beneficial)AWS experience Experience ...

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 ...

with a client in a high-traffic, heavily regulated consumer sector. You'll own how the platform runs. That means the Kubernetes infrastructure behind model serving, agent orchestration and batch inference. It means the CI/CD pipelines that ship models, agents, tools and prompt changes. It means … operations: Experience operating model serving, LLM inference or MLOps pipelines in production is a strong plus. Familiarity with model APIs, vector databases, MCP or orchestration frameworks helps. Networking and security: VPC design, service mesh and zero‐trust patterns. Domain & Sector Experience Regulated industries: Experience running platforms under strict ...

with a client in a high-traffic, heavily regulated consumer sector. You'll own how the platform runs. That means the Kubernetes infrastructure behind model serving, agent orchestration and batch inference. It means the CI/CD pipelines that ship models, agents, tools and prompt changes. It means … operations: Experience operating model serving, LLM inference or MLOps pipelines in production is a strong plus. Familiarity with model APIs, vector databases, MCP or orchestration frameworks helps. Networking and security: VPC design, service mesh and zero‐trust patterns. Domain & Sector Experience Regulated industries: Experience running platforms under strict ...

with a client in a high-traffic, heavily regulated consumer sector. You'll own how the platform runs. That means the Kubernetes infrastructure behind model serving, agent orchestration and batch inference. It means the CI/CD pipelines that ship models, agents, tools and prompt changes. It means … operations: Experience operating model serving, LLM inference or MLOps pipelines in production is a strong plus. Familiarity with model APIs, vector databases, MCP or orchestration frameworks helps. Networking and security: VPC design, service mesh and zero‐trust patterns. Domain & Sector Experience Regulated industries: Experience running platforms under strict ...

Shipping features end-to-end across Rebel, used daily by real teams Making autonomous product and technical decisions Working directly with LLMs, agents, and MCP integrations Contributing to our open-screen ecosystem Helping shape engineering standards and culture as the team grows Mentoring and setting the bar for quality About ...

relationships and communicate technical recommendations, risks and progress to stakeholders. Drive the adoption of GenAI within Quality Engineering, including tools such as Playwright with MCP, GitHub Copilot and Claude. Contribute to internal QE communities, standards and best practices. What were looking for Strong hands-on experience in Quality Engineering ...

relationships and communicate technical recommendations, risks and progress to stakeholders. Drive the adoption of GenAI within Quality Engineering, including tools such as Playwright with MCP, GitHub Copilot and Claude. Contribute to internal QE communities, standards and best practices. What were looking for Strong hands-on experience in Quality Engineering ...

years in a leadership capacity. Deep understanding of global Funds markets with a deep understanding of Funds data landscape Strong grasp of data science, MCP AI, and agentic automation concepts and how they transform financial data workflows. Proven track record of defining and executing innovative product strategies that deliver measurable ...

visibility intelligence to win every moment of discovery - across AI and traditional search. the company's API-first architecture connects structured data to APIs, MCP servers, and generative interfaces, so partners and developers can build purpose-built experiences on the same infrastructure powering the company's own products. Thousands ...

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 ...

Developer Enablement, Enterprise Architecture, the AI Foundry, and engineering leadership to build the infrastructure that makes agentic development effective at FactSet specifically. This includes MCP servers and skills that give AI tools access to FactSet's standards, libraries, and institutional knowledge; verification and trust layers that catch hallucination and enforce ...

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 … provide high-quality grounding for AI systems and keep them aligned with evolving data sources and business logic. Implement tool-calling, function-calling, and MCP-style integrations so agents can safely take actions and interact with the systems around them. Create reusable agent templates, modular components, and paved-path patterns ...

information is presented to a senior audience. Platform-level automation. Experience with low-code/no-code platforms (e.g. n8n, Copilot Studio), MCP, LLM observability tools (e.g. Langfuse), or managed model APIs (Azure AI Foundry, OpenAI, Anthropic).Modern development practices. Familiarity with any of: Docker, Terraform, CI/ ...

them reliably in production. Track record of building on shared enterprise data platforms (for example Snowflake) and delivering through modern interfaces such as MCP, rather than building parallel infrastructure. Team Building & People Leadership Proven ability to build, grow, and retain a high‐performing teams, defining roles, skills, and competencies … developing existing talent into a world‐class capability. Experience designing the operating model of a technical team, its ways of working, delivery cadence, quality standards, and tooling, in a product operating model context. A coaching leadership style that raises the technical bar while creating an environment where ...

other systems of record and engagement. Design integration patterns that let AI capabilities interoperate safely with enterprise data, identity,workflowand process automation platforms– including MCP and A2A and how these capabilities enable an enterprise Agent mesh. Understand the data,governanceand change implications of embedding AI into mission-critical enterprise processes. … Orchestration & control plane Architect the orchestration and control-plane layer that governs how AI capabilities are exposed,routedand managed across the enterprise. Design MCP, AI and LLM gateway layers for secure,observableand policy-controlled access to models and tools. Design agent and skills registries — including recommending suitable technologies and patterns ...

their impact. We value your AI skills if you have built fluency across the agentic engineering toolchain — coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agentic frameworks — and can describe projects where you shipped real customer-facing work with these tools. You know how to drive … integration Experience with test-driven development, CI/CD workflows, and continuous integration tools (Jenkins, GitLab) Experience integrating AI technologies such as Skills, Agents, MCP Servers, or RAG pipelines Understanding of authentication and authorization approaches including OpenID and SAML Experience deploying cloud-native services on AWS with containerized infrastructure ...

three pillars of the AI strategy; AI for Service Delivery, AI for Digital 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 (Agentic Thames and the Agentic … systems and institutionalise the multi-platform Agent Builder for craft-your-own agents. Agentic Platform & AI ArchitectureOwn 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 ...

architecture and delivery of multi-agent AI systems for high-value business use cases. defining engineering standards across orchestration architectures, context engineering, MCP integration, memory design, fine-tuning, evaluation frameworks and multimodal systems. building reusable AI capabilities including agent tools, MCP servers, skills and shared engineering assets. establishing quality … powered systems at enterprise scale. Expert-level Python engineering. Multi-agent architectures and orchestration frameworks. Context engineering, retrieval systems and memory architectures. MCP server and SKILL.md design and enterprise tooling integration. Model fine-tuning at scale (PEFT/(Q)LoRA). Building end-to-end voice AI pipelines ...

management, forecasting and optimisation (including usage‐based pricing shifts), and the staff access model. This includes: AI safety for internal use: sandboxing, policy, integrations, MCP governance, usage guardrails and monitoring expectations. The three platform pillars: control, monitoring and sandboxing (LLM and MCP gateway, anonymisation, local hardening, sandboxed cloud agent hosting … built or significantly scaled a team. Current, hands‐on fluency with the modern AI stack: frontier model platforms, agentic tooling and harnesses, MCP and integration patterns, evals, prompt and context engineering. AI security and governance: sandboxing, gateways (LLM, MCP, AI), DLP, guardrail enforcement and data‐boundary reasoning; able ...

agent approaches, and define evaluation, safety, and monitoring strategies for production use Own production deployment patterns including containerization, continuous integration and delivery, automated testing, model and prompt registries, model and version governance, monitoring and alerting, and rollback strategies Architect and deploy scalable, reliable, and secure machine learning … evaluation and safety patterns Familiarity with agentic building blocks and standards, including orchestration frameworks, context and memory management, and interoperability protocols such as MCP Experience with machine learning frameworks and data science packages such as PyTorch, TensorFlow, scikit‐learn, NumPy, pandas, SciPy, and statsmodels Experience deploying to AWS, including ...

agent approaches, and define evaluation, safety, and monitoring strategies for production use Own production deployment patterns including containerization, continuous integration and delivery, automated testing, model and prompt registries, model and version governance, monitoring and alerting, and rollback strategies Architect and deploy scalable, reliable, and secure machine learning … evaluation and safety patterns Familiarity with agentic building blocks and standards, including orchestration frameworks, context and memory management, and interoperability protocols such as MCP Experience with machine learning frameworks and data science packages such as PyTorch, TensorFlow, scikit-learn, NumPy, pandas, SciPy, and statsmodels Experience deploying to AWS, including ...

management, forecasting and optimisation (including usage-based pricing shifts), and the staff access model. This includes: AI safety for internal use : sandboxing, policy, integrations, MCP governance, usage guardrails and monitoring expectations. The three platform pillars : control, monitoring and sandboxing (LLM and MCP gateway, anonymisation, local hardening, sandboxed cloud agent hosting … built or significantly scaled a team. Current, hands-on fluency with the modern AI stack: frontier model platforms, agentic tooling and harnesses, MCP and integration patterns, evals, prompt and context engineering. AI security and governance: sandboxing, gateways (LLM, MCP, AI), DLP, guardrail enforcement and data-boundary reasoning; able ...

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 ...