Sr Lead Software Engineer - Agentic AI
Confirmed on the employer's own hiring board on Oct 25, 2026. First seen by Alion on Sep 9, 2026. Campbell Global scores A on the Alion truth index.
Be an integral part of Asset & Wealth Management (AWM) Technology, where you will help shape the future of Private Bank digital platforms through engineering excellence, intelligent automation, and AI-driven innovation.
As a Senior Lead Software Engineer for Agentic AI at JPMorganChase within the Asset & Wealth Management (AWM) Technology organization, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
As an Agentic AI Senior Lead Software Engineer within AWM, Private Banking Web, you will lead the development of production-grade solutions that harness agentic AI, large language models, and automation to elevate how advisors and clients experience Private Banking. Partnering with product, design, architecture, cybersecurity, and data teams, you will solve complex business challenges, accelerate delivery, and drive measurable impact for one of the world's most influential financial institutions.
Job responsibilities
Lead full-stack design, development, and delivery of secure, scalable Private Banking Web applications across front-end, back-end, service, data, and cloud tiers
Build high-quality production software using Python, Java, modern web technologies, RESTful APIs, microservices, and event-driven architecture
Develop responsive and reliable web experiences that integrate user interfaces, APIs, business services, data platforms, and enterprise systems
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Architect agentic AI solutions that leverage LLMs, AI agents, orchestration frameworks, tool calling, workflow automation, and enterprise data sources
Integrate AI-enabled capabilities that enhance personalization, search, insights, recommendations, and self-service across Private Banking Web experiences
Drive technical decisions across application architecture, cloud services, security controls, performance, observability, reliability, and production readiness
Review designs and code, debug complex full-stack issues, strengthen code quality, and enforce secure coding practices across globally distributed teams
Collaborate with product, design, data, architecture, cybersecurity, risk, and business stakeholders to deliver solutions aligned to enterprise standards
Mentor engineers, influence technical direction, and promote effective use of AI-assisted development tools, reusable skills, and engineering accelerators
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Demonstrate advanced hands-on full-stack software engineering expertise, with a proven track record of designing and delivering production-grade enterprise applications
Possess strong hands-on development experience with Python, Java, modern web technologies, RESTful APIs, microservices, distributed systems, and data integration patterns
Experience building secure multi-tier applications using AWS or other cloud platforms, including compute, storage, networking, identity, security, observability, and deployment automation services
Ability to apply deep knowledge of application security, authentication, authorization, data protection, secure SDLC practices, vulnerability remediation, and financial services control expectations
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
Develop agentic AI solutions using LLMs, prompt engineering, retrieval-augmented generation, AI agents, tool calling, workflow orchestration, and frameworks such as LangGraph, Semantic Kernel, MCP, A2A, CrewAI
Design reliable integrations using messaging, streaming, GraphQL, gRPC, WebSocket, relational databases, NoSQL platforms, caching, search, and event-driven technologies
Deliver engineering quality through automated testing, CI/CD pipelines, Infrastructure as Code (IaC), containerization, Docker, Kubernetes, monitoring, logging, and production support
Lead technical teams in a global agile environment, influence architecture decisions, communicate with senior stakeholders, and mentor engineers across multiple disciplines
Preferred qualifications, capabilities, and skills
Experience with Private Banking, Wealth Management, MarTech, client engagement, advisor platforms, or regulated financial services technology
Experience using AI-assisted engineering tools, coding agents, reusable skills, prompt libraries, and developer productivity platforms to improve delivery efficiency
Experience implementing responsible AI practices, including model evaluation, guardrails, observability, data privacy, human-in-the-loop review, explainability, and risk-aware deployment
Ability to modernize legacy platforms by decomposing applications, improving API strategy, increasing automation, and migrating workloads to cloud-native architectures
Communicate complex technical concepts clearly to engineering, product, business, cybersecurity, and senior leadership audiences