Leads software engineering for secure, scalable financial technology applications. Responsibilities include designing, developing, testing, reviewing, debugging, and troubleshooting production code; improving automation, continuous delivery, and operational stability; implementing AI-assisted engineering practices; and evaluating vendor and internal architectural solutions. Requires Python, AWS cloud, software development lifecycle, data or machine-learning pipelines, and technical leadership experience, with preferred expertise in Terraform, Kubernetes, Docker, Kafka, MLOps, and LLMOps.
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As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank, Securities Services Technology, 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 conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank, Securities Services Technology, 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 conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Develops secure and high-quality production code, and reviews and debugs code written by others
- 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.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Extensive experience in building and running AWS/public cloud based applications
- Solid programming skills with Python
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Proficiency in automation and continuous delivery methods and all aspects of the Software Development Life Cycle
- Experience of pipelines and DAG's (Directed Acyclic graph) for processing data and/or machine learning
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence and machine learning.)
- In-depth knowledge of the financial services industry and their IT system.
- 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.
Preferred qualifications, capabilities, and skills
- Experience with Cloud services, Infrastructure as Code (IaC) and containerized application development
- Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock
- Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps)
- Experience working on AIML systems and/or prior experience collaborating with data scientists
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