Lead Software Engineer - Agentic AI

JOB DESCRIPTION

We have an opportunity to impact your career and provide an adventure where you can push the limits of what’s possible.

As a Lead Software Engineer at JPMorganChase with in Enterprise Technology Cloud Foundational Services, 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. As a core technical contributor, you are responsible for delivering critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job Responsibilities

• Execute creative software solutions, design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions or break down technical problems.
• Deliver end-to-end solutions in the form of cloud-native, microservices-based applications, leveraging the latest technologies and best industry practices.
• Design, develop, and deploy resilient, fault-tolerant applications on AWS, leveraging services such as ECS, EKS, Lambda, RDS, S3, and API Gateway to ensure high availability and operational excellence.
• Provision, manage, and maintain cloud infrastructure using Terraform, enforcing infrastructure-as-code best practices, reusable module design, and consistent environment management across development, staging, and production.
• Architect and build agentic AI applications using modern frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, or AWS Bedrock Agents), enabling autonomous, multi-step reasoning and tool-use capabilities within production-grade systems.
• Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Lead communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
• Promptly investigate and resolve issues, ensuring they do not resurface.
• Design and build scalable, secure, and reliable solutions by leveraging modern architectural patterns that ensure zero-downtime releases and optimize data performance.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
• Proficiency in back-end technologies (e.g., Python, Flask, Django) with experience building microservices-based applications; for full-stack roles, proficiency also includes front-end technologies (e.g., HTML, CSS, JavaScript, TypeScript, React, Angular).
• Strong hands-on experience developing and deploying resilient applications on AWS, including deep familiarity with core AWS services, high-availability design patterns, disaster recovery strategies, and AWS Well-Architected Framework principles.
• Strong understanding and hands-on experience in cloud infrastructure provisioning using Terraform, including writing modular, reusable infrastructure-as-code, managing remote state, and integrating Terraform into CI/CD pipelines.
• Hands-on experience building agentic AI applications using one or more frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, AWS Bedrock Agents), including designing m…