Senior Lead Software Engineer - AI/ML Deep Learning & GPU ML Serving

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 JPMorgan Chase within the Commercial & Investment Bank, Payments 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

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
• Develops secure and high-quality production code, and reviews and debugs code written by others
• 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.
• 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

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Relevant professional software development experience with focus on ML systems. Experience contributing to the system design or architecture (architecture, design patterns, reliability and scaling) of new and current systems, particularly ML serving platforms.
• Strong proficiency in Python with experience in ML frameworks (TensorFlow, PyTorch, or similar). Experience in Cloud Native Technologies (Docker, Kubernetes, specifically EKS)
• Experience deploying and managing GPU workloads in Kubernetes environment. Hands-on experience with ML model serving frameworks (TorchServe, TensorFlow Serving, Triton Inference Server, or similar).
• Experience optimizing deep learning models for production inference (model quantization, batching, caching strategies). Experience in partnering with product and program management teams. Strong proficiency with Java, Python, Scala, or C++
• Experience in building scalable, low-latency systems based on web services and APIs. Expertise in application, data, and infrastructure architecture disciplines. Experience with modern microservices architecture
• Experience with Cassandra or equivalent NoSQL databases for high-throughput data access. Understanding of GPU resource management, scheduling, and cost optimization. Ability to lead the design of large-scale systems and to reason about the challenges and tradeoffs
• 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.
• Experience with graph neural networks (GNNs) and graph processing frameworks (DGL, PyTorch Geometric, NetworkX). Experience serving large-scale models (billions of parameters) with optimization techniques

Preferred qualifications, capabilities, and skills

• Experience with one or more public clouds (AWS, GCP), with strong AWS knowledge preferred

• Financial services experience is a plus
• Understanding of payments systems, fraud detection, or financial modeling is highly desirable
• Experience handling PII data and building secure ML systems