Java AI Sr Lead Software Engineer

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 Senior Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Deposits Automation & Reliability 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. 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

• Develops and codes in Java with some Python as an individual contributor solving complex technical issues and driving resolution across applications, services and platforms
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale
• Operates as a senior hands-on individual contributor, writing code, solving complex technical issues, and driving resolution across applications, services, and platforms
• Leads the design, development and modernization of software solutions that improve reliability, scalability, automation and operational efficiency across complex technology platforms
• Builds, enhances and maintains automation frameworks, AI-driven engineering solutions, internal platforms and reusable tools for engineering teams
• Supports performance testing, resiliency testing, chaos testing, and controlled failure scenarios to validate system behavior under production-like conditions
• Leads and mentors engineers through code reviews, technical guidance, problem-solving support, and promotion of strong engineering practices
• Communicates technical strategy, reliability risks, resolution plans, and delivery progress clearly to engineering teams, product partners, and leadership

• Defines and promotes release readiness checks, quality gates, test coverage expectations, reliability standards, and validation approaches for cloud-native platforms

   

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience   
• Advanced proficiency in programming languages such as Java and some Python
• Deep hands-on (8+ years) software engineering experience with the ability to design, code, debug, test, and resolve complex technical problems independently
• Possess a strong ownership mindset with a track record of solving difficult problems, improving engineering practices, and driving technical issues to closure across complex environments
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls
• Successful experience in mentoring engineers, raising technical standards, communicating tradeoffs, and influencing stakeholders across engineering and leadership audiences
• Strong analytical skills with the ability to investigate failures, identify root causes, recommend durable fixes, and drive issues to closure
• Practical experience modernizing cloud-native applications, improving platform reliability, or leading automation-driven engineering initiatives
• Experience with microservices, RESTful APIs, relational databases, NoSQL databases, messaging frameworks, and event-driven architectures
• Proven ability to lead technical direction, influence architecture, and drive engineering improvements across teams or complex service areas
  
  
   

• Experience with performance testing, load testing, resiliency validation, or reliability tools (JMeter, Blazemeter, Gatling, etc.)
• Experience with observability and monitoring tools such as (Splunk, Dynatrace, Grafana, CloudWatch etc.)
• Cloud certification in AWS, GCP, or Azure