The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is pivotal in advancing the firm's data and analytics capabilities, ensuring strong adherence to data and AI risk and control while enabling the data and analytics strategy for superior decision-making and business outcomes to serve our clients and markets. By leveraging data and AI/ML, the CDAO develops innovative solutions to support commercial goals, enhance productivity, and manage risks. The Asia Pacific CDAO advances the firm’s data and analytics strategy, platforms, solutions, capabilities, and governance to deliver trustworthy, responsible, innovative, and commercially valuable outcomes across the APAC markets and businesses.
As a Product Solutions Manager / Agent Platform Engineer in the CDAO Fusion Platform team, you will bridge client solutioning and hands-on engineering to accelerate adoption of Fusion’s agent capabilities. You will partner with Sales and client-facing teams to define and configure solutions for key client relationships and prospects, acting as the voice of the customer by translating needs into clear product feedback and roadmap inputs. In parallel, you will design and build production-grade AI agents and multi-agent architectures on the Fusion platform, working directly with Line of Business engineering teams to solve complex integrations, harden solutions for regulated production environments, and develop reusable reference implementations that scale adoption across the firm.
Job responsibilities
- 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.
- Designs and builds 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 with client teams and LoB engineers to understand pain points, refine and debug solutions in forward-deployed engagements, ship production agents on Fusion, and relay critical feedback to Product to inform the strategic roadmap.
- Architects multi-agent systems: define agent boundaries, orchestration patterns, context passing, tool surface exposure, and state management for regulated production workloads.
- Develops and maintains reference implementations and SDK playbooks that translate platform capabilities into reusable, opinionated engineering patterns for LoB consumption.
- Contributes to MCP SDK design and tooling — define tool schemas, validate tool surface security, and build integrations between agents and enterprise systems.
- Integrates RAG pipelines into agent workflows — manage knowledge base configuration, chunking strategies, retrieval tuning, and drift monitoring in production.
- Identifies and closes 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 agent builds — provide hands-on guidance on blast radius containment, human oversight hooks, and production hardening.
- Contributes 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.
Required qualifications, capabilities, and skills
- Bachelor’s in Computer Science, Artificial Intelligence, Mathematics, or related field.
- 8+ years of software engineering experience, with at least 3 years focused on AI/ML systems, GenAI application development, or agent-based architectures in production.
- Experience in problem-solving across multiple teams and a cluster of products.
- Extensive experience engaging clients throughout the sales cycle and tailoring preconfigured solutions to address complex needs.
- Demonstrated prior experience working in a highly matrixed and complex organization.
- 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 oversimplifying or losing technical accuracy.
Preferred qualifications, capabilities, and skills
- 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.