26 to 50 of 532 Model Context Protocol Jobs

LLMs, retrieval pipelines, agentic workflows, and platforms such as Anthropic Claude. Experience designing and deploying AI agents, leveraging Model Context Protocol (MCP), and orchestrating enterprise-grade AI solutions will be highly valued. Acting as a bridge between business and technology, you’ll work across product, data science … deploying agentic AI solutions using platforms such as Anthropic Claude, including AI agents, tool orchestration, reasoning workflows, Model Context Protocol (MCP), and enterprise-scale automation use cases. Hands-on experience building AI-enabled systems, such as LLM pipelines, document extraction, structured output generation, AI-assisted analytics, prediction ...

this role. Working with ML flow. Designing developing and testing agent build in Google ADK Working with Model Context Protocol (MCP) and creating servers. Other technology we use includes Neo4j, Postgres, Kafka and libraries such as FAST API. Build and maintain CI/CD pipelines to support … others can follow them. Generative AI: Familiarity with GenAI concepts including system prompts, Agent Development Kit (ADK), and Model Context Protocol (MCP) for data and service sharing. Google ADK: Hands‐on experience using Google ADK. Neo4j or other graph databases Team Leadership: Experience managing or coordinating software ...

tools, retrieval systems and configurations for production use. Integrate AI systems with enterprise APIs, databases, applications and data platforms. Develop reusable connectors, APIs and Model Context Protocol tools. Build automated CI/CD, testing, configuration management and deployment workflows. Implement authentication, access control, secrets management, security … with Azure, AWS or Google Cloud, Kubernetes, Helm, Terraform and CI/CD technologies. Experience with LangGraph, AutoGen, smolagents or similar frameworks. Experience with Model Context Protocol, model‐serving platforms, vector databases and LLM gateways. Experience implementing AI evaluation, observability, tracing and performance monitoring. Experience with ...

systems across customer-facing and operational domains. Defining and owning Aviva’s canonical engineering patterns for applied AI, including orchestration architectures, context engineering, MCP integration, memory design, fine-tuning and multimodal systems. Building and maintaining a reusable library of agent tools, MCP servers, skills and shared engineering assets adopted … Context & Retrieval: Advanced context engineering expertise, including multi-hop retrieval, knowledge graphs (Neptune/Nebula), structured memory injection and dynamic prompt construction. MCP & Tooling: Advanced knowledge of Model Context Protocol (MCP), designing and implementing server architectures for enterprise tooling integration. Model Fine-Tuning: Experience ...

governance, and delivery support. CE AI owns and operates core AI platforms including LSEG AI Assist, the Question Answering Service (QAS), and the Internal MCP Gateway. Rather than delivering individual business use cases end‐to‐end, the team enables product engineering groups across LSEG to expose knowledge, data, and actions … agents in a consistent, governed, and repeatable way. The team operates a Central MCP Delivery model: building critical MCP tools and services "for" product teams where required, while simultaneously defining standards, patterns, and platform capabilities that allow teams to progressively move towards self‐service contribution. LSEG AI Assist/ ...

patterns, governance, and delivery support.CE AI owns and operates core AI platforms including LSEG AI Assist, the Question Answering Service (QAS), and the Internal MCP Gateway. Rather than delivering individual business use cases end‐to‐end, the team enables product engineering groups across LSEG to expose knowledge, data, and actions … agents in a consistent, governed, and repeatable way.The team operates a Central MCP Delivery model: building critical MCP tools and services “for” product teams where required, while simultaneously defining standards, patterns, and platform capabilities that allow teams to progressively move towards self‐service contribution.LSEG AI Assist/Internal MCP ...

governance, and delivery support. CE AI owns and operates core AI platforms including LSEG AI Assist, the Question Answering Service (QAS), and the Internal MCP Gateway. Rather than delivering individual business use cases end‐to‐end, the team enables product engineering groups across LSEG to expose knowledge, data, and actions … agents in a consistent, governed, and repeatable way. The team operates a Central MCP Delivery model: building critical MCP tools and services "for" product teams where required, while simultaneously defining standards, patterns, and platform capabilities that allow teams to progressively move towards self‐service contribution. LSEG AI Assist/ ...

optimize prompts and interaction strategies for LLMs to achieve desired outcomes in agentic systems. Integrate various tools, APIs, and data sources, potentially utilizing MCP Servers, to enhance model context and tool integration. Develop robust, scalable, and maintainable code in Python for all components of the agentic applications, from … translate requirements into technical solutions. Stay up-to-date with the latest research and developments in generative AI, LLMs, agentic systems, and protocols like MCP, evaluating their potential for our products. Contribute to the architecture and technical roadmap of our AI initiatives. Mentor junior engineers and share knowledge within ...

Senior Lead Software Engineer at JPMorganChase within the Corporate Technology Data and Analytics Services team, you will be a founding contributor to the Context Plane - a greenfield platform that connects the firm's data mesh and knowledge sources to AI agents and large language model tools. You will … knowledge into a knowledge graph and vector store Build and evolve the serving layer - including graph and vector retrieval, GraphRAG, response assembly, and a Model Context Protocol endpoint consumed by downstream agents Extract and promote reusable components into a shared core library, reducing duplication across the platform ...

agent orchestration framework (such as LangGraph or first‐party agent SDKs) and understand tool calling and the Model Context Protocol (MCP) Evaluation engineering: you have built or operated eval harnesses (golden datasets, regression gates in CI, LLM‐judge calibration) and can explain why they gate merges Post … compatible object storage as the data platform, Neo4j Enterprise as the semantic knowledge graph, an agent memory plane serving episodic and precedent memory over MCP, MCP‐native connectors, OpenTelemetry and Grafana for observability, all on CNCF‐conformant Kubernetes with Helm and Argo CD, deployable to any hyperscaler or on‐prem. ...

translate capability changes into delivery capability and commercial offerings. Building and Embedded Delivery Build production AI systems hands-on: agents, tool use, retrieval pipelines, MCP integrations and enterprise workflow connectors. Embed with client teams to map how work actually runs across functions, then design and deliver against it. Lead … Intellectual Property Own and productise Invoka's AI delivery IP: the accelerator framework, reusable components, prompt libraries and evaluation suites. Take our in-house MCP servers from internal tools to supported, documented, sellable asset, and own its roadmap. Develop our temporal data and change analysis capability, which we deliver ...

translate capability changes into delivery capability and commercial offerings. Building and Embedded Delivery Build production AI systems hands-on: agents, tool use, retrieval pipelines, MCP integrations and enterprise workflow connectors. Embed with client teams to map how work actually runs across functions, then design and deliver against it. Lead … Intellectual Property Own and productise Invoka's AI delivery IP: the accelerator framework, reusable components, prompt libraries and evaluation suites. Take our in-house MCP servers from internal tools to supported, documented, sellable asset, and own its roadmap. Develop our temporal data and change analysis capability, which we deliver ...

collaborative culture through communities of practice and emerging-technology events. You will explore and operationalize emerging patterns such as agent-to-agent communication, model context protocols, and agentic orchestration, turning early-stage concepts into scalable, production-ready capabilities. Job ResponsibilitiesDesign, develop, and troubleshoot software solutions using creative approaches … environment using modern programming and database querying languagesExperience with containerizationKnowledge of agent-to-agent (A2A) communication conceptsFamiliarity with Model Context Protocol (MCP)Experience with agentic orchestrators, personal AI assistants, or AI skills development#LLMSuiteEngineering_jobsJ.P. Morgan is a global leader in financial services, providing strategic advice and products ...

collaborative culture through communities of practice and emerging-technology events. You will explore and operationalize emerging patterns such as agent-to-agent communication, model context protocols, and agentic orchestration, turning early-stage concepts into scalable, production-ready capabilities. Job Responsibilities Design, develop, and troubleshoot software solutions using creative … programming and database querying languages Experience with containerization Knowledge of agent-to-agent (A2A) communication concepts Familiarity with Model Context Protocol (MCP) Experience with agentic orchestrators, personal AI assistants, or AI skills development J.P. Morgan is a global leader in financial services, providing strategic advice and products ...

collaborative culture through communities of practice and emerging‐technology events. You will explore and operationalize emerging patterns such as agent‐to‐agent communication, model context protocols, and agentic orchestration, turning early‐stage concepts into scalable, production‐ready capabilities. Job Responsibilities Design, develop, and troubleshoot software solutions using creative … programming and database querying languages Experience with containerization Knowledge of agent‐to‐agent (A2A) communication concepts Familiarity with Model Context Protocol (MCP) Experience with agentic orchestrators, personal AI assistants, or AI skills development #J-18808-Ljbffr ...

Lead for FactSet’s Banking and Insurance offerings across Europe, the Middle East, and Asia‐Pacific and global leadership responsibilities for FIG AI and MCP initiatives. This senior role has two complementary mandates. First, you will lead international client engagement, market development, and product opportunity identification for FactSet’s Banking … Insurance offerings. Second, you will lead Deep Sector FIG’s contribution to FactSet’s broader AI and MCP initiatives globally. Partner with FactSet’s AI, Applications, Content, and Engineering teams to ensure Banking and Insurance content is structured, contextualized, discoverable, and reliable for AI‐enabled and agentic workflows. Engage directly ...

stability. You will help deliver GenAI services using public cloud capabilities. You will explore and operationalize emerging patterns such as agent-to-agent communication, model context protocols, and agentic orchestration, turning early-stage concepts into scalable, production-ready capabilities. You will contribute as a team player who seeks … corporate environment using modern programming and database querying languages, including containerization Knowledge of agent-to-agent (A2A) communication, Model Context Protocol (MCP), AI skills development, personal AI assistants, or agentic orchestrators #LLMSuiteEngineering_jobs #J-18808-Ljbffr ...

graph, and hybrid approaches. Design multi-agent systems with modern orchestration and reasoning architectures, including human-in-the-loop and autonomous frameworks. Define memory, context, planning, tool-use, and reasoning strategies for agentic systems. Stay technical. Contribute to architecture, critical-path code, design reviews, and the hardest technical problems. … Semantic Kernel, Strands. Advanced retrieval : Graph RAG, knowledge graphs, context graphs, hybrid search, vector databases. Agentic patterns : multi-agent architectures, planning and reasoning, MCP (Model Context Protocol). Model customization: fine-tuning, distillation, and model evaluation at scale. What success looks like in this ...

high-stakes domains: guardrails, autonomy levels, kill switches, and human-in-the-loop approval Experience with agent runtimes - sandboxing and isolation, tool invocation (e.g. Model Context Protocol), multi-agent orchestration, and reasoning-loop control A track record of building or operating an evaluation and analytics harness … next in how AI gets embedded in complex, regulated systems Bonus Points For Experience building or defining agent-to-agent or tool-integration protocols (Model Context Protocol and similar) at platform scale Knowledge of evaluation frameworks and fine‐tuning techniques. Familiarity with graduated autonomy models and agent ...

with clarity, speed, and confidence. If you are excited about this opportunity but do not meet every single requirement, We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity. Skills and Competencies … issue resolution and feedback collection Preferred: Familiarity with financial data, company information, credit research, corporate ownership or risk management solutions Preferred: Experience working with Model Context Protocol services, agentic workflows, generative artificial intelligence platforms or embedded data solutions Preferred: Familiarity with basic coding and analytics principles, including ...

Claude on Bedrock, Gemini, Azure OpenAI), selected per use case for quality, latency and cost. Interfaces: Tools and Model Context Protocol (MCP) for integration; standards-based APIs and secure auth for client systems. What you'll do Conceptualize fast: embed with stakeholders, frame a business process … 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 ...

Claude on Bedrock, Gemini, Azure OpenAI), selected per use case for quality, latency and cost. Interfaces: Tools and Model Context Protocol (MCP) for integration; standards-based APIs and secure auth for client systems. What you'll do Conceptualize fast: embed with stakeholders, frame a business process … 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 ...

Claude on Bedrock, Gemini, Azure OpenAI), selected per use case for quality, latency and cost. Interfaces: Tools and Model Context Protocol (MCP) for integration; standards-based APIs and secure auth for client systems. What you'll do Conceptualize fast: embed with stakeholders, frame a business process … 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 ...

system that solves it. Architecture, implementation, evaluation, and production operation. You are responsible for the system working, not just for your code compiling. Design context and retrieval strategies. What goes into the context window and what stays out is the most consequential design decision in an AI system. … thinking or external contributions in AI engineering (talks, writing, open source) Experience with multi‐agent coordination: task decomposition, handoff, shared state Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards What We Are Not Looking For Pure ML researchers without production engineering experience. ...

bridge between cutting‐edge AI technologies and existing high‐code and low‐code platforms, the role focuses on AI enablement rather than foundational model building, ensuring intelligence is thoughtfully integrated into systems and workflows. The AI Engineer designs, develops, and assures AI-enabled solutions, improves automation efficiency, elevates engineering … automation platforms (e.g., Power Automate, n8n, Workato) by embedding intelligent processing and applying agentic patterns where relevant. Implement Model Context Protocol (MCP) servers for secure AI‐to‐system connectivity. Lead AI‐based document parsing and intelligent data extraction initiatives. Contribute to educating and enabling Enterprise Capabilities areas ...