Senior Product Manager (MCP Integrations)
Posted Updated
Description
- As part of the LSEG AI team, we are hiring a Senior Product Manager – MCP Integrations & Agentic Workflows to own the strategy, design and delivery of integration patterns and agentic orchestration capabilities that connect LSEG’s MCP tools into multi-step, autonomous AI workflows
- This role is responsible for defining how LSEG’s MCP tools are composed, chained, and orchestrated by AI agents—across partner ecosystems (Claude, Copilot Studio, OpenAI, open-source frameworks), third-party platforms, and internal agentic applications. You will shape how LSEG’s financial intelligence is consumed not just as individual tool calls, but as part of sophisticated, multi-step reasoning pipelines that automate real financial workflows
- You will work at the frontier of agentic AI, defining how autonomous systems reliably interact with LSEG’s data and analytics at enterprise scale
- Integration Strategy & Ecosystem
- Define the integration roadmap for LSEG MCP tools across AI platforms—including Anthropic (Claude), Microsoft (Copilot Studio, Azure AI), OpenAI, Google, and open-source agent frameworks (LangChain, CrewAI, AutoGen)
- Establish integration patterns, reference architectures, and best practices for connecting LSEG MCP tools to third-party agent orchestrators and workflow engines
- Identify and prioritise strategic integration partnerships that expand the reach and utility of LSEG’s MCP ecosystem
- Agentic Workflow Design
- Define product patterns for multi-step, multi-tool agentic workflows—tool chaining, conditional branching, parallel execution, human-in-the-loop checkpoints, and error recovery
- Design workflow templates and reference implementations for high-value financial use cases: morning briefings, equity research generation, portfolio rebalancing analysis, credit surveillance, and macro scenario modelling
- Own the product requirements for orchestration reliability—ensuring agents handle tool failures, timeouts, ambiguous responses, and guardrail triggers gracefully
- Developer & Partner Experience
- Design onboarding experiences, sample workflows, and integration guides that enable external developers and partners to build agentic applications on LSEG MCP tools
- Define and maintain demo environments, sandbox configurations, and reference prompts that showcase LSEG’s MCP capabilities within agent frameworks
- Build and nurture a developer community around LSEG’s MCP integrations, gathering feedback and surfacing adoption blockers
- Reliability & Safety for Agentic Systems
- Define guardrails, approval gates, and safety patterns for autonomous AI agents interacting with LSEG financial data—ensuring agents cannot take unintended actions or surface misleading outputs
- Partner with AI Governance and Model Risk to establish standards for agent auditability, decision traceability, and human oversight in agentic financial workflows
- Design monitoring and alerting for agentic workflow health—tracking completion rates, failure modes, tool call patterns, and cost efficiency across agent-driven pipelines
- Cross-Functional Collaboration
- Work closely with the Financial Domain Tools PM to ensure tools are designed for composability and chaining within agent workflows
- Partner with the MCP Platform PM to ensure the infrastructure supports orchestration patterns—session continuity, context passing, and multi-tool transactions
- Collaborate with commercial and business development teams to position LSEG’s agentic MCP capabilities with strategic partners and enterprise clients
- Engage with LSEG product teams across divisions to identify and instrument high-value agentic use cases
- Measures of Success
- Integration Reach & Adoption
- Number of AI platforms and agent frameworks with active, supported LSEG MCP integrations
- Growth in agentic workflow invocations across partner ecosystems
- Developer adoption metrics—onboarding completions, active integrations, community engagement
- Workflow Quality & Reliability
- Agentic workflow completion rates, failure rates, and mean-time-to-recovery across supported patterns
- Reduction in agent-driven errors, hallucination-related tool misuse, and unintended data exposure incidents
- Quality and coverage of workflow templates and reference implementations for key financial use cases
- Strategic Impact
- Number of strategic partner integrations (AI platforms, fintechs, enterprise clients) enabled through MCP
- Contribution to new commercial revenue streams driven by agentic MCP capabilities (partnerships, Model-as-a-Service, API consumption)
- Influence on LSEG’s positioning as the leading financial data provider for AI agent ecosystems
- Experience in product management for integrations, APIs, developer platforms, or workflow/orchestration products
- Familiarity with AI/LLM ecosystems and agent frameworks (LangChain, CrewAI, AutoGen, Copilot Studio, or equivalent)
- Experience designing developer-facing products—SDKs, documentation, reference implementations, and sandbox environments
- Ability to work across ecosystem boundaries with external partners, developer communities, and platform providers
- Understanding of agentic AI patterns—tool chaining, multi-step reasoning, autonomous decision-making, and human-in-the-loop workflows
- Exposure to MCP, function-calling, or tool-use patterns in production LLM applications
- Background in financial services technology, capital markets workflows, or enterprise data integration
- Experience with partnership or ecosystem development in a platform or marketplace context
- Understanding of AI safety, guardrails, and governance considerations for autonomous agent systems