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Sr. Lead Software Engineer: Backend Engineering

JPMorgan Chase
New York City, USA
Full-time
Senior · 5+ years experience
Onsite
Discovered 2 weeks ago
airflowawsecseksflinkjava

Full Job Posting

Job responsibilities

  • Develops secure and high-quality production code, and reviews and debugs code written by others
  • Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
  • Drives decisions that influence the product design, application functionality, and technical operations and processes
  • Serves as a function-wide subject matter expert in one or more areas of focus
  • Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
  • Influences peers and project decision-makers to consider the use and application of leading-edge technologies
  • 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.

Required qualifications, capabilities, and skills

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Hands-on software development experience in an Object-Oriented programming language (such as Java, C++, Python)
  • Requires depth of knowledge and experience in three of the four following areas, with developing knowledge in the others.
  • High-throughput, low-latency micro service development leveraging AWS services such EKS, ECS, Fargate, ELB, etc.
  • High throughput near real time stream processing with services such Kinesis, Flink, ECS, EKS, etc.
  • Caching systems such as Redis to lower database load
  • Large volume data processing using Ray, Spark and similar technologies
  • Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc]
  • Solid understanding of agile methodologies and knowledge of SDLC including CI/CD, Application Resiliency, and Security.
  • 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.

Preferred qualifications, capabilities, and skills

  • Experience with recommendation and personalization systems

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