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 and Investment Bank's Engineering and Architecture 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 break down technical problems
- Develops secure high-quality production code, and reviews and debugs code written by others
- Lead a local team of software engineers and applied AI/ML practitioners, driving accountability and engagement within a global organization.
- Collaborate with product and business teams to set technical vision and execute strategic roadmaps for AI-driven software solutions.
- Translate business requirements into robust software and AI/ML specifications, define milestones, and ensure timely delivery using Agile methodologies.
- Architect, design, and develop secure, high-quality production software systems using Java or Python, integrating AI/ML techniques such as LLMs, Generative AI, and coding assistants
- Identify opportunities to automate and remediate recurring issues, improving overall system reliability.
- Design experiments, implement algorithms, validate results, and productionize performant, scalable, and trustworthy AI/ML solutions.
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts and 5+ years applied experience
- Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Auto-GPT)
- Advanced proficiency in Java or Python for software system development; strong grasp of software engineering best practices, system design, application development, testing, and operational stability
- Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals and coding assistants
- Managing and mentoring software engineering or AI/ML teams, with experience as a hands-on practitioner delivering production-grade solutions
- 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
- Good understanding of data structures, algorithms, and practical machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn)
- Proficiency in automation, continuous delivery (CI/CD), and cloud-native development (preferably AWS)
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Hands-on practical experience delivering system design, application development, testing, and operational stability
Preferred qualifications, capabilities, and skills
- Experience working at code level with advanced AI/ML business applications (e.g., LLMs, Generative AI, NLP)
- AWS Certifications (Solution Architect Associate or Professional) are advantageous
- In-depth knowledge of the financial services industry and their IT systems
- Practical cloud native experience