JPMorgan Chase's Infrastructure Platforms team within the Corporate Sector is looking to enhance, create, and deliver high-quality technology products in a secure, stable, and scalable manner, specifically focusing on developing critical technology solutions across numerous technical domains to support the firm's business goals, including AI and machine learning workloads.
Requirements
- Proficiency in at least one programming language, such as Python, Go, Java, or C.
- Proficiency in automation and continuous delivery methods.
- Proficient in all aspects of the Software Development Life Cycle.
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
- Foundational understanding of machine learning concepts, including transformer architecture, ML training, and inference.
- Experience in solutions design and engineering, containerization (Docker, Kubernetes), and cloud service providers (AWS, Azure, GCP).
- Experience with Infrastructure as Code.
Responsibilities
- Execute creative software solutions, including design, development, and technical troubleshooting, with the ability to think beyond conventional approaches to build solutions or resolve technical problems.
- Develop secure, high-quality production code, and review and debug code written by others.
- Identify opportunities to eliminate or automate the remediation of recurring issues to enhance the overall operational stability of software applications and systems.
- Lead evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented assessments of architectural designs, technical credentials, and their applicability within existing systems and information architecture.
- Lead communities of practice across Software Engineering to promote awareness and adoption of new and leading-edge technologies.
- Develop and deploy cloud infrastructure platforms that are secure, scalable, and optimized for AI and machine learning workloads.
- Collaborate with AI teams to understand computational needs and translate these into infrastructure requirements.
Other
- Formal training or certification in software engineering concepts with 5+ years of applied experience.
- Hands-on practical experience in delivering system design, application development, testing, and ensuring operational stability.
- Contribute to a team culture of diversity, equity, inclusion, and respect.
- Foundational understanding of NVIDIA GPU infrastructure software (e.g., DCGM, BCM, Triton Inference).
- Hands-on experience with machine learning frameworks such as PyTorch and TensorBoard.
