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Lead Software Engineer

Lead Software Engineer

JP Morgan Chase & Co.
5-7 Years
Not Disclosed
Early Applicant
  • Posted a month ago
  • Be among the first 10 applicants

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 within the Commercial & Investment Bank Payments Technology team, 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
  • Demonstrable depth in AI/ML systems. Strong experience with AWS
  • Advanced proficiency in Java, with strong experience in at least one additional programming language (e.g., Python) and modern front-end technologies (e.g., React)
  • Practical understanding of LLM orchestration, RAG, tool calling, prompt engineering, and dynamic reasoning
  • Proficiency in automation and continuous delivery methods. Proficient in all aspects 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.)
  • In-depth knowledge of the financial services industry and their IT systems. Advanced in one or more programming language(s)
  • 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 vector databases and search (e.g., FAISS, OpenSearch/Elasticsearch, equivalents)
  • Experience with Observability Stack.
  • Knowledge of Splunk, DynaTrace, Geneos, etc

Key Skills

LLM orchestration

RAG tool calling

dynamic reasoning

AI ML systems

data sensitivity considerations

secure handling of inputs outputs

approved AI-assisted software development tools

CI CD

continuous delivery methods

prompt engineering

Application Resiliency

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