Leads software engineering delivery within an agile team, designing, developing, testing, debugging, and supporting secure, scalable applications. Establishes effective and responsible AI-assisted engineering practices, including code generation, review, testing, troubleshooting, and validation. Automates recurring remediation, improves operational stability, evaluates vendor and startup technologies, and contributes to AWS web application development, Java engineering, cloud deployments, DevOps, and machine learning or LLM integrations.
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As a Lead Software Engineer at JPMorganChase within the Asset and Wealth Management, 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 JPMorganChase within the Asset and Wealth Management, 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
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Experience in building applications using LLM driven code generation such as Claude Code or Microsoft copilot
- 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
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Practical knowledge of web application development on AWS
Good Experience in Java development, cloud deployments and in continuous delivery and DevOps tooling
Preferred qualifications, capabilities, and skills
Experience in building, evaluating and deploying Machine learning Models into Production
Experience in integration of standard AI/ML/LLM software solutions, design, development, and technical troubleshooting.
Experience working with Large Language Models (LLM)/GenAI technology like OpenAI API, (GPT models), HuggingFace models etc. having solid understanding of Transformers architecture.
Experience in Natural Language Processing and NLP tools and libraries like Spacy.
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