Vice President Product Solutions - Chief Data Analytics Office Fusion Platform
Product, Data Science
- Leads solutioning and the adoption of existing and upcoming client-facing products and capabilities while defining and configuring optimal solutions that address clients’ needs and objectives
- Serves as a subject matter expert on a defined set of products and capabilities with a deep understanding of our clients’ needs and current industry trends
- Supports Sales in pricing, pipeline planning, account planning, and upskilling the team on product knowledge by collaborating on training and collateral materials
- Engages with client teams to better understand pain points and refine solutions while regularly communicating critical client feedback to Product teams to inform the strategic product roadmap
- Design and build production-grade AI agents using Agent Studio, SmartSDK, RAG SDK, and MCP SDK including orchestrator/sub-agent architectures, tool-calling patterns, parallel execution loops, and write-back integrations.
- Partner directly with LoB (Line of Business) engineering teams in Forward Deployed Engineering engagements — embed alongside their engineers, debug live integration issues, and jointly ship production agents on Fusion.
- Architect multi-agent systems: define agent boundaries, orchestration patterns, context passing, tool surface exposure, and state management for regulated production workloads.
- Develop and maintain reference implementations and SDK playbooks that translate platform capabilities into reusable, opinionated engineering patterns for LoB (Line of Business) consumption.
- Contribute to MCP SDK design and tooling — define tool schemas, validate tool surface security, and build integrations between agents and enterprise systems. Integrate RAG pipelines into agent workflows — manage knowledge base configuration, chunking strategies, retrieval tuning, and drift monitoring in production.
- Identify and close capability gaps in agent observability, evaluation, and error recovery — work with Platform Engineering to surface and prioritize field-driven requirements. Participate in architecture reviews for high-complexity LoB (Line of Business) agent builds — provide hands-on guidance on blast radius containment, human oversight hooks, and production hardening.
- Contribute to the Agent Deployment Risk Framework — translate governance requirements into engineering constraints that ship as code, not documentation. Maintain personal technical depth as the agent stack evolves — MCP, tool-calling patterns, multi-modal inputs, model gateway integration, and evaluation frameworks.
- 5+ years of experience or equivalent expertise in problem-solving across multiple teams and a cluster of products
- Extensive experience working in a sales cycle and engaging with clients on a regular basis
- Experience modifying preconfigured solutions to meet complex problems
- Demonstrated prior experience working in a highly matrixed and complex organization
- 5+ years of software engineering experience, with at least 3 years focused on AI/ML systems, GenAI application development, or agent-based architectures in production.
- Strong Python fluency — you write production-quality Python, not just scripts. Experience with async patterns, SDK extension, and framework-level engineering is expected.
- Hands-on experience building agents or agentic workflows — tool-calling, orchestration, multi-step reasoning loops, and agent-to-agent communication patterns. Working knowledge of LLM APIs and agent frameworks (LangChain, LangGraph, AutoGen, CrewAI, or equivalent) not just tutorials, but actual production systems.
- Experience integrating RAG pipelines: vector stores, embedding models, chunking strategy, retrieval evaluation, and production monitoring.
- Ability to architect systems at the component level — define interfaces, trace data flows, identify failure modes, and reason about blast radius in distributed agent systems.
- Comfortable operating in complex enterprise environments with governance, compliance, and model risk constraints — you understand why these exist and how to engineer around them, not just complain about them.
- Strong written and verbal communication - you can explain an agent architecture to a senior engineer and to a business MD, without changing the truth in between.
- Direct experience with MCP (Model Context Protocol) designing tool schemas, building MCP servers, managing tool surface exposure, or integrating MCP into an agent platform.
- Experience in regulated industries — financial services, healthcare, or government — with practical exposure to model risk management, audit trails, and compliance-driven engineering constraints.
- Familiarity with agent security concerns: prompt injection, tool misuse, over-privileged tool access, and blast radius containment strategies.
- Experience building evaluation frameworks for LLM-based systems not just benchmarks, but production-grade evaluation pipelines with structured outputs and regression tracking.
- Exposure to cloud-native AI infrastructure — managed model endpoints, model gateways, token/cost observability, and multi-tenant serving considerations.
- Experience contributing to developer-facing SDK or platform tooling — designing APIs that other engineers consume, writing documentation that actually gets used, and iterating based on adoption signal.
- Familiarity with responsible AI practices as they apply to agents: human oversight requirements, escalation paths, intervention hooks, and auditability standards.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.
Lead solutioning, collaborate with Sales, and deliver client-centric product solutions in a dynamic organization