Lead Software Engineering - Python

JPMorganChase•Jersey City, NJ

About The Position

Be an integral part of an agile team that’s constantly pushing the envelope to enhance, build, and deliver trusted, market-leading technology products in a secure, stable, and scalable way. As a Lead Software Engineer at JPMorganChase within Infrastructure Platforms, you will make a significant business impact through your skills and contributions, applying technical expertise and problem-solving methodologies to address a diverse array of challenges across multiple technologies and applications.

Requirements

Formal training or certification on software engineering concepts and 5+ years of hands-on experience in software engineering, including system design, application development, testing, and operational support.
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).
Hands-on development experience with Java and Spring/Spring Boot for building and supporting services.
Experience working with cloud platforms (e.g., AWS, Azure, GCP), distributed systems, and web technologies, including RESTful APIs and web services, WebSockets, and JSON.
Hands-on experience designing and building scalable applications using SQL and NoSQL databases.
Experience with agile development methodologies (e.g., Scrum) and an understanding of the software development life cycle.
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
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.

Nice To Haves

Strong soft skills, including presentation, negotiation, mentoring, and stakeholder management.
Strong problem-solving, analytical, and communication skills.
Ability to drive broader impact by sharing and contributing best practices.
Experience in the banking domain.
AWS certification.

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.
Use domain modeling techniques to build best-in-class business products, structuring software for clarity, testability, and evolution.
Develops secure high-quality production code, and reviews and debugs code written by others.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
Adds to team culture of diversity, opportunity, inclusion, and respect.
Design and build solutions that avoid single points of failure using scalable architectural patterns.
Design and build scalable, secure, and reliable solutions by leveraging modern architectural patterns that ensure zero-downtime releases and optimize data performance.
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.