Make your mark by using Agentic AI to shape how teams build trusted and AI-ready data at scale as they go through the Data Development Lifecycle (DDLC). Join the Firmwide Chief Data Office at JPMorganChase where your work directly improves speed, quality, and governance in data delivery. Bring your product mindset and technical fluency to help design a platform that grows your career through high-impact, enterprise-wide collaboration.
Job Summary
As an Agentic AI Lead for the DDLC at JPMorganChase, you will help build and scale an agentic artificial intelligence solution that enables Data Product Managers to execute the end-to-end Data Development Lifecycle (DDLC) – from ideation and modeling through publishing, governance, and consumption. You will translate business needs into a clear technical roadmap, validate engineering delivery, and improve how teams publish and consume governed data products. You will partner closely with engineering and business stakeholders to define success measures, evaluate capability releases, and drive adoption across multiple business areas.
This role sits at the intersection of business strategy, data product delivery, and software engineering execution. Your knowledge of agentic best practices will help build out the architecture for this solution including skills, tools and the harness. Your knowledge of data engineering/science best practices will help translate business needs into technical solutions. You will run structured discovery with business stakeholders to understand current workflows and friction points, then convert those insights into prioritized requirements and measurable outcomes. You will also support executive communications by synthesizing progress into concise narratives, metrics, and roadmap updates.
Job Responsibilities
- Define end-to-end requirements for the build of an agentic solution for the data persona that enables end-to-end data product development, in alignment with firmwide data guidelines and standards.
- Lead discovery with business teams to document current workflows, identify friction points, and translate needs into a prioritized intake backlog. Also demonstrate how the agent addresses these pain points.
- Design and run structured user-feedback loops (interviews, shadowing sessions, usability tests, telemetry review) to continuously refine tool capabilities against real DPM workflows.
- Partner with engineering leads to convert business needs into well-scoped technical requirements and discrete, assignable tasks for data engineers and AI engineers.
- Validate delivered capabilities by reviewing agent behaviors, generated outputs, and integrated workflows to ensure they match intent and agreed standards.
- Define and track acceptance criteria, success metrics, and evaluation frameworks (e.g., agent task-completion rate, output quality, human-in-the-loop intervention rate) for each capability release.Coordinate delivery execution through backlog refinement, sprint planning support, release readiness checks, and dependency management across teams.
- Contribute to the multi-year roadmap for scaling the agentic tool from PoC to a firmwide platform – including capability expansion, LOB onboarding sequencing, and platform hardening.
- Develop a working point of view on the target agentic architecture: orchestration patterns, scheduling, agent-to-agent communication, memory/state management, tool registries, and evaluation infrastructure.
- Identify reusable components, shared services, and integration points with existing firmwide data platforms and skills repositories.
- Partner with governance, risk, controls, legal, and compliance stakeholders to ensure responsible use and appropriate controls are embedded into the solution. Collaborate and align with data engineers, data scientists, product managers, architects, and technology partners to achieve business outcomes.
- Drive coordination and communication with senior stakeholders to advance the DDLC strategy and secure ongoing sponsorship. Produce executive-ready materials: roadmap updates, PoC readouts, adoption metrics, and business-value narratives.
Required qualifications, capabilities and skills
- Demonstrated understanding of agentic artificial intelligence patterns, including single-agent, multi-agent, and orchestrated approaches.
- Working knowledge of generative artificial intelligence building blocks, including large language models, prompt design, retrieval augmented generation, context design, evaluation methods, and safety controls.
- Hands-on experience using modern generative artificial intelligence development tools (for example, enterprise-approved coding assistants or agent-enabled integrated development environments).
- Working knowledge of data engineering concepts, including deployment pipelines, continuous integration and continuous delivery, environment promotion, testing strategies, and observability.
- Working knowledge of the data development lifecycle, including data modeling, schema and contract design, metadata management, data quality, lineage, and publishing standards.
- Proven ability to translate ambiguous business problems into clear technical work items, milestones, and measurable outcomes.
- Experience running pilots in a large, regulated enterprise environment, including stakeholder alignment and release discipline.
- Strong communication and presentation skills, with the ability to connect senior stakeholder priorities to engineering execution.
Track record of end-to-end delivery in data, data science, or data product work in consulting or internal consulting contexts.
Preferred qualifications, capabilities and skills
- Experience with modern data platforms such as Databricks or Snowflake.
- Familiarity with evaluation operations for generative artificial intelligence (for example, test harnesses, benchmarking, monitoring, and continuous improvement practices).
- Advanced degree in a quantitative, computer science, or data-related discipline.
- Familiarity with financial services environments, including operating in control-conscious delivery models.
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
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.
Skills Required
- Demonstrated understanding of agentic artificial intelligence patterns (single-agent, multi-agent, orchestrated approaches).
- Working knowledge of generative AI building blocks, including large language models, prompt design, retrieval augmented generation, context design, evaluation methods, and safety controls.
- Hands-on experience using modern generative AI development tools (enterprise-approved coding assistants or agent-enabled integrated development environments).
- Working knowledge of data engineering concepts: deployment pipelines, CI/CD, environment promotion, testing strategies, and observability.
- Working knowledge of the data development lifecycle: data modeling, schema and contract design, metadata management, data quality, lineage, and publishing standards.
- Proven ability to translate ambiguous business problems into clear technical work items, milestones, and measurable outcomes.
- Experience running pilots in a large, regulated enterprise environment, including stakeholder alignment and release discipline.
- Strong communication and presentation skills, with ability to connect senior stakeholder priorities to engineering execution.
- Track record of end-to-end delivery in data, data science, or data product work in consulting or internal consulting contexts.
- Experience with modern data platforms such as Databricks or Snowflake.
- Familiarity with evaluation operations for generative AI (test harnesses, benchmarking, monitoring, continuous improvement).
- Advanced degree in a quantitative, computer science, or data-related discipline.
- Familiarity with financial services environments and control-conscious delivery models.
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
JPMorganChase Insights
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.
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