Lead Software Engineer - AI/ML Developer Lead

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Software Engineer II at JPMorganChase within the Consumer & Community Banking, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role. 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 - Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications - 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 - Applies technical troubleshooting to breakdown solutions and solve technical problems of basic complexity - Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development - Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems 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 - Hands-on experience building, deploying, and maintaining machine learning platforms or infrastructure - Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL) - Practical experience with cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure - 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 - Proficient in all aspects of the Software Development Life Cycle - Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security - In-depth knowledge of the financial services industry and their IT systems Practical cloud native experience Preferred qualifications, capabilities, and skills: - Familiarity with Databricks for scalable data engineering and ML platform integration - Experience working with Snowflake for cloud-based data warehousing and analytics - Exposure to Snorkel AI for programmatic data labeling and training data management - Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow) - Familiarity with feature stores, model registries, and ML metadata management - Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation) - Experience with RESTful APIs and microservices architectures