Lead Software Engineer-AI Platform Engineer

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 JPMorgan Chase within the Corporate Sector, specifically as a part of the Infrastructure Platforms team, you will play a crucial role in an agile team committed to enhancing, creating, and delivering high-quality technology products in a secure, stable, and scalable manner. Your role as a vital technical contributor will involve developing critical technology solutions across numerous technical domains within various business functions, all aimed at supporting the firm's business goals.

Job 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.
• Contribute to a team culture of diversity, equity, inclusion, and respect.
• 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.
• Monitor, manage, and optimize cloud resources to maximize performance and minimize costs.
• Design and implement continuous integration and delivery pipelines for machine learning workloads.
• Develop automation scripts and infrastructure as code to streamline deployment and management tasks.

Required Qualifications, Capabilities, and Skills:

• 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.
• 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.
• Deep understanding of cloud component architecture: Microservices, Containers, IaaS, Storage, Security, and routing/switching technologies.

• 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.
• Proficiency with observability tools like Prometheus and Grafana.
• Experience in ML Ops and related tooling, including MLflow.
• Background in high performance computing and ML frameworks (e.g., vLLM, , Slurm).
• Strong knowledge of network architecture, database programming (SQL/NoSQL), and data modeling.
• Familiarity with cloud data services, big data processing tools, and Linux environments (scripting and administration).