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 Lead Software Engineer at JPMorgan Chase within the Commercial and Investment Banking team, you will play a pivotal role in an agile team, enhancing and delivering secure, stable, and scalable technology products.

As a core technical contributor, you will drive critical technology solutions across multiple technical areas, supporting the firm's business objectives.

Job Responsibilities


* Lead the design, development, and troubleshooting of software solutions, applying innovative approaches to complex technical challenges.


* Write secure, high-quality production code and maintain algorithms integrated with firm systems.


* Produce architecture and design artifacts for advanced applications, ensuring compliance with design constraints.


* Analyze and visualize large, diverse data sets to improve software applications and systems.


* Identify and resolve hidden issues and patterns in data to enhance code quality and system architecture.


* Collaborate with software engineering communities to explore and adopt emerging technologies.


* Guide system design and architecture discussions, focusing on reliability and scalability.


* Optimize deep learning models for production inference, including quantization and batching.


* Deploy and manage GPU workloads in Kubernetes environments.


* Build scalable, low-latency systems using web services and APIs.


* Partner with product and program management teams to deliver business-driven solutions.

Required qualifications, capabilities, and skills


* Formal training or certification on software engineering concepts and 5+ years applied experience


* Professional software development experience, with emphasis on ML systems.


* Strong proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch, or similar).


* Experience with cloud technologies (Docker, Kubernetes, EKS) and public clouds (AWS, GCP).


* Hands-on experience with ML model serving frameworks (TorchServe, TensorFlow Serving, Triton Inference Server).


* Experience deploying and managing GPU workloads in Kubernetes.


* Familiarity with scalable, low-latency systems based on web services and APIs.


* Experience with NoSQL databases (Cassandra or equivalent) for high-throughput data access.


* Understanding of GPU resource management and cost optimization.


* Experience with modern microservices architecture.


* Ability to lead the design of large-scale systems and evaluate tradeoffs.

Preferred qualifications, capabilities, and skills


* MS/PhD in Computer Science, Machine Learning, or a related field.


* Proficiency in Java, Python, Scala, or C++.


* Experience with graph neural networks and graph processing frameworks (DGL, PyTorch Geometric, NetworkX).


* Knowledge of GPU programming ...