Lead Software Engineer - Fullstack Java/AWS/AI/ML

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 & Community Banking, 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


* 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.


* Full Stack Application Development Develop and maintain front-end applications using modern JavaScript frameworks (e.g., React, Angular, or Vue.js), build robust backend services in Java and Python, and ensure seamless integration between UI layers, APIs, middleware, and data stores.


* AI/ML Model Development & Edge Deployment Build, train, fine-tune, and optimize AI/ML models using industry-standard tools and frameworks (e.g., PyTorch, TensorFlow, Hugging Face, ONNX, TensorRT).

Package and deploy models for inference on edge devices with constrained compute resources, as well as in cloud-hosted backend environments.

Manage the full model lifecycle including experimentation, versioning, evaluation, and monitoring.


* Cloud Infrastructure & Operations Leverage hands-on AWS expertise to provision, configure, and manage cloud infrastructure supporting data pipelines and model serving.

This includes working with services such as EC2, S3, Lambda, ECS/EKS, SageMaker, Kinesis, IAM, CloudWatch, and related tooling.

Ensure operational excellence through monitoring, alerting, cost optimization, and infrastructure-as-code practices.


* Data Pipeline Development & Integration Design, implement, and maintain scalable, fault-tolerant data pipelines that ingest, transform, and deliver data across distributed systems.

Architect event-driven and streaming data flows using Apache Kafka and other messaging queue technologies to support real-time and near-real-time processing requirem...