326 to 350 of 573 Model Context Protocol Jobs

production delivery Able to communicate your 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-office ...

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 ...

into direct input to the CEO, CPO, CTO, and Product organization on roadmap priorities, platform strategy, competitive positioning, and emerging customer requirements, including Enterprise MCP, agentic AI frameworks, and governance. Field Enablement. Coach Solutions Engineering, Customer Success, and Sales leadership on executive engagement, strategic technology positioning, and boardroom storytelling. Build ...

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/ ...

spoken. Preferred Qualifications Fine‐tuning, RLHF, or model customization. Vector databases (Pinecone, Weaviate, or FAISS) and embedding retrieval. Building agent workflows with MCP servers or modern agent frameworks. Conversational AI and speech (speech‐to‐text/text‐to‐speech) and multilingual systems. Experience in the payments or fintech industry. … Spanish proficiency. Tech Stack Languages — Python (AI/ML), Go and Kotlin (platform services). AI & LLMs — LLM APIs and open models, agent frameworks, MCP, RAG. AI evaluation & observability — LangFuse, LangSmith, Braintrust, MLflow. Data & retrieval — vector databases, feature stores. Messaging — Apache Kafka, SQS. Databases — PostgreSQL, Redis. Infrastructure — AWS, Kubernetes, Docker ...

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 ...

reshape how software is designed, built, tested and delivered. These roles will work across strategic product transformation, cloud and data platforms, enterprise AI services, context and knowledge engineering, quality, experience design and AI-enabled developer tooling. This is more than an opportunity to join an individual project … machine learning certifications or Bedrock specific training Hands on experience with LLM applications, agent frameworks or orchestration tooling, e.g. Bedrock AgentCore, Strands, LangGraph or MCP Experience building or contributing to an internal developer platform or shared enterprise capability Experience with event driven architecture and messaging platforms (e.g. Kafka ...

with experience developing and deploying solutions using LLMs, RAG architectures, and modern AI frameworks and protocols (Strands, FastMCP, LangChain, LlamaIndex, Hugging Face, A2A, MCP).Ability to translate multi-layered business problems into technical solutions with experience working directly with business stakeholders to understand requirements and deliver production systems. Experience … Python, cloud platforms (AWS/Azure), APIs, and full-stack development to rapidly prototype and productionise AI applications. Experience with MLOps practices including model deployment, monitoring, evaluation, and CI/CD pipelines with delivering AI solutions from concept to production. Some other highly valued skills may include: Excellent communication ...

with experience developing and deploying solutions using LLMs, RAG architectures, and modern AI frameworks and protocols (Strands, FastMCP, LangChain, LlamaIndex, Hugging Face, A2A, MCP).Ability to translate multi-layered business problems into technical solutions with experience working directly with business stakeholders to understand requirements and deliver production systems. Experience … Python, cloud platforms (AWS/Azure), APIs, and full-stack development to rapidly prototype and productionise AI applications. Experience with MLOps practices including model deployment, monitoring, evaluation, and CI/CD pipelines with delivering AI solutions from concept to production. Some other highly valued skills may include: Excellent communication ...

tool-calling, structured outputs, planning/state management, retries, guardrails, and orchestration layers on top of LLM provider APIs (Anthropic, OpenAI, open-weight models, MCP). Ship internal AI automations to remove engineering/support/growth toil by integrating with existing tooling (GitHub, Slack, Intercom, Jira) or building bespoke … practices (golden datasets, eval harnesses, tracing, graceful failure modes, cost/latency controls). Deep LLM-API fluency (Anthropic/OpenAI/open-weight), MCP, agent frameworks, and familiarity with AI coding tools (Claude Code, Cursor). Cloud and delivery experience: AWS and/or DigitalOcean, Docker, CI/ ...

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 ...

capability, including designing, developing and productionising AI-enabled solutions using retrieval-augmented generation, embeddings, vector search, context/prompt engineering, agentic AI patterns, MCP-based extensibility, and Microsoft ecosystem AI tooling. The role ensures these solutions are grounded in reliable, well-governed and secure data foundations. What … solutions for resilience, observability, cost-efficiency and maintainability across production environments. Design and implement custom AI-enabled systems and components, including retrieval-augmented generation, MCP-based extensibility, agentic AI architectures, embeddings, vector-based retrieval and context/prompt engineering. Build and productionise AI solutions using the Microsoft ecosystem, including ...

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 ...

practical LLM workflows, agents, skills and tools that support advisory teams, client delivery and internal operations. Implement reusable AI capabilities using OpenWebUI, LiteLLM and MCP tools. Work across prompting, context engineering, structured outputs, evaluation, provider routing and human‐in‐the‐loop workflows. Move useful applications and automations quickly from … 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 ...

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 ...

secure, observable, reusable and governed by default. You will also contribute to the production foundations needed to operate AI capabilities reliably, including LLMOps, model access patterns, prompt and agent lifecycle practices, observability and secure enterprise integration. What you’ll be doing Designing and building AI platform capabilities on Azure … evaluation practices for agent configurations, prompts and AI workflows, including testing, controlled change and release readiness Designing secure tool-access patterns for agents, including MCP/tool abstraction, credential management and enterprise API integration. Contributing to AgentOps and GenAIOps capabilities, including telemetry, run history, task outcomes, error analysis and feedback ...

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 ...