Client360 is one of JPMorganChase Commercial & Investment Bank’s strategic pillars, building the future of our business and unifying the data fabric of all client activity. Combined with external data, Client360 leverages this unique data asset to deliver insight and enterprise rewiring at scale.
Are you passionate about Agentic AI and operational transformation? As the Agentic AI Operations Associate, you will help drive the build and day-to-day operation of agentic AI solutions, enabling intelligent automation across Client360. You’ll work closely with product, architecture, engineering, platform operations, and data operations teams to implement agent-led workflows and ensure they are scalable, reliable, well-controlled, and measurable.
This role is suited to an engineer who can independently translate intent into working software, proactively improve delivery quality, and reduce the burden on senior team members by understanding the technical requirements without extensive explanation.
Job responsibilities
- Build and deliver software that enables agentic AI operations for Client360 (e.g., services, workflow components, integrations, automations, internal tools), working within established platform standards.
- Partner with architecture and engineering leads to implement the target-state Agentic AI workbench and integrated control environment, including connectivity patterns, orchestration hooks, and evaluation/monitoring integrations.
- Own small-to-medium engineering deliverables end-to-end: requirements clarification, design approach, implementation, testing, release, and post-release support.
- Contribute to code reviews and quality gates; proactively identify broken or risky changes and either fix them or propose specific remediations.
- Support operational readiness by contributing to runbooks, instrumentation/telemetry, dashboards/alerts (where applicable), incident learnings, and ongoing production hygiene.
- Contribute to CI/CD and release practices by working with automated build/test pipelines, deployment workflows, environment promotion, deployment verification, and rollback readiness.
- Help coordinate integration with platform and data operations teams to pivot workflows into agent-led operations while maintaining reliability, auditability, and a strong user experience.
- Support risk/control-aligned delivery by contributing to traceability and evidence (requirements → build → deployment → monitoring), in partnership with relevant SMEs and control functions.
- Assist in establishing and tracking KPIs (stability, adoption, time-to-value, automation effectiveness) and using data to drive continuous improvement.
- Collaborate with stakeholders across Markets, Payments, Global Banking, and the Private Bank to drive consistency, adoption, and continuous improvement of agentic AI solutions.
Required qualifications, capabilities, and skills
- 2–4+ years of real-world software delivery experience as an engineer (growth engineering, product engineering, full-stack development, backend, or platform), including shipping to production and supporting what you ship.
- Demonstrated ability to operate with minimal technical hand-holding: understands technical requirements quickly and can independently implement fixes and improvements with good judgment.
- Hands-on experience using AI coding assistants in day-to-day development (e.g., GitHub Copilot and/or Claude Code) to accelerate delivery, with an understanding of their limitations and the need for rigorous review/testing. (Use of any specific tool must align with JPMorganChase-approved tooling and policy.)
- Strong engineering fundamentals: version control, code review practices, testing strategy, debugging, and operational excellence habits.
- Familiarity with Continuous Integration and Continuous Delivery (CI/CD), including automated build/test pipelines, deployment workflows, release hygiene, and safe change practices (environment promotion, rollbacks, and deployment verification).
- Strong communication and collaboration skills, with the ability to work effectively across product, architecture, engineering, and operations teams and to explain technical concepts to non-technical stakeholders.
- Working knowledge (or strong willingness to learn) of delivery in regulated environments, including secure SDLC practices, data governance, logging/monitoring, and operational risk considerations.
Preferred qualifications, capabilities, and skills
- Experience building or operating AI-enabled systems (agentic workflows, orchestration frameworks, LLM-enabled automation) and/or observability for AI/automation in production.
- Experience working in a highly matrixed, complex organization and/or within a financial institution.
- Degree in Computer Science, Engineering, or a related field is beneficial, but is not a substitute for hands-on engineering delivery experience.
- Experience ensuring customers and stakeholders are engaged and positive about delivered solutions through strong rollout, enablement, and feedback loops.