Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Lead Software Engineer at JPMorgan Chase within the Corporate 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
- Design and implement enterprise-grade Machine Learning platforms capable of deploying and running predictive models at scale.
- Develop web applications using service-oriented and microservices architecture with Java and Python frameworks.
- Integrate solutions with AWS Cloud Services, including compute, storage, databases, and security components.
- Build tools and automation solutions for monitoring, provisioning, and streamlining processes, services, and reporting.
- Establish comprehensive monitoring and alerting frameworks to ensure optimal performance, scalability, availability, and reliability.
- 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.
- Partner closely with Product teams to design, build, and deliver capabilities through agile sprints.
- Leverage strong operational insights to recommend improvements to upstream products, processes, and policies that enhance the user experience.
- Utilize AI agents and emerging technologies to build prototypes for demonstration purposes, presenting to peer groups, business partners, and senior leadership.
- Deliver high-quality results within tight deadlines while maintaining a strong focus on code optimization, performance tuning, and engineering best practices.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Experience in building high-volume, low-latency, high-throughput transactional systems.
- Experience in building microservices using Java/Spring Boot and Python/Fastapi/Flask
- Experience with AWS services - S3, DynamoDB, ECS, EKS, RDS, Lambda, and ALB/NLB.
- Experienced with pair programming agents such as GitHub Copilot/Claude code to accelerate prototyping.
- 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
- Strong knowledge of RDBMS, schema design, SQL, query optimization, indexing, joins, and JDBC.
- Agile Development experience with SCRUM or similar methodologies
- Completed AWS Developer or Solution Architect Certification
- Excellent problem-solving skills and the ability to think critically and creatively
Preferred qualifications, capabilities, and skills
- Experience in MLOps and building model serving applications
- Experience with artificial intelligence and machine learning tools and framework in development.
- Certification in Databricks
- Experience in observability and production management tools (ex. Splunk / Dynatrace / Grafana)