J.P. Morgan Asset Management’s RFP and DDQ function operates at significant scale supported by writers across the US, EMEA, and APAC. The firm has invested meaningfully in AI-assisted capabilities, and the next step is to unlock higher automation and quality by upgrading the content foundation itself. This role is accountable for transforming the current content management system from a document-oriented content library into a governed, machine-readable knowledge management (KM) platform that can reliably power retrieval and generation at scale. You will set the strategy, build the operating model, and partner with technology to build the platform.

• KM platform strategy and roadmap: Define and execute the multi-phase roadmap to convert IRL into a governed, machine-readable KM platform. Establish content schema standards, taxonomy/ontology direction, versioning rules, and minimum metadata requirements that improve retrieval accuracy and generative performance.
• Freshness and coverage governance: Design and implement freshness SLAs by content domain (e.g., firm, product, investment process, performance narrative, risk/compliance disclosures), with automated enforcement mechanisms and clear accountability for approvals and renewals. Build instrumentation to measure coverage gaps, duplication, and “content debt,” and drive an investment plan to close gaps.
• Learning loop architecture: Build the feedback infrastructure that captures downstream edits, win/loss outcomes, and convergent adviser variants. Convert these signals into a structured curation queue with prioritization logic, routing, and measurable throughput. Ensure learnings become durable knowledge objects, not one-off revisions.
• AI automation performance ownership: Own the KPI for first-draft AI fill rate. Diagnose where content-layer constraints (missing structure, inconsistent phrasing, stale artifacts, ungoverned variants, weak citations/attribution, fragmented ownership) are limiting automation, and drive remediation through schema, governance, and curation.
• Cross-functional stakeholder leadership: Partner with Technology, Compliance, Investment Specialists, and Client Advisers to embed the KM platform into the end-to-end RFP workflow. Translate user needs into platform requirements, align on controls, and drive adoption through governance forums and change management.
• Measurement, reporting, and executive communication: Own the KM platform OKRs and reporting cadence—hours saved, fill rate improvements, freshness SLA compliance, learning loop throughput, reuse rates, and quality indicators. Provide clear, data-backed narratives to senior leadership on progress, risks, trade-offs, and decisions required.

• 8–12 years of experience in knowledge management, content strategy, information architecture, or related disciplines within financial services.
• Demonstrated success building or leading a content/knowledge governance function at scale, ideally supporting asset management, institutional sales, client reporting, or other regulated content environments.
• Practical understanding of how LLMs and retrieval-augmented generation (RAG) systems depend on structured, governed content—plus experience translating that understanding into schemas, operating controls, and measurable performance improvements.
• Proven leadership in operational transitions: redefining roles, redesigning workflows, establishing training and QA, and driving adoption across distributed teams.
• Strong data orientation: ability to define KPIs, build instrumentation, diagnose root causes, and make prioritization decisions based on signal.
• Experience working with offshore/near-shore content teams, including governance models that balance scale, quality, and accountability across time zones.
• Ability to influence across Technology, Compliance, and front-office stakeholders with clarity on risks, controls, and business outcomes.
• MBA or advanced degree preferred but not required.

J.P. Morgan Asset Management is at a strategic inflection point: the AI tooling layer is in place, and meaningful productivity gains are already visible—but the function is approaching an automation ceiling because the underlying content foundation was not designed as a machine-readable knowledge platform. This role builds the governed content layer that makes AI durable and scalable: structured schemas, freshness enforcement, and a closed learning loop that continuously improves quality and coverage. Done well, this transformation will raise first-draft fill rates, reduce cycle time, strengthen controls, and modernize the writer population into a high-leverage governance function that compounds value with every RFP and DDQ.