As a Lead Software Engineer at JPMorgan Chase within the Corporate and Investment Banking Applied Artificial Intelligence and Machine Learning team, you will play a pivotal role in transforming the operations of the world's largest bank. You will collaborate with software engineers, data scientists, and line of business teams to develop and sustain resilient infrastructure for AI/ML solutions.
Job Responsibilities
* Design, deploy, and operate application infrastructure using Amazon EKS/ECS.
* Build and maintain foundational data storage infrastructure, including Aurora Postgres, OpenSearch, and Amazon S3.
* Deploy and operate open-source AI/ML software, ensuring scalability, security, and operational efficiency.
* Automate infrastructure provisioning and management using Terraform, Helm, Spinnaker, and related tools.
* Implement and uphold resiliency best practices, including defining and meeting SLAs/SLOs.
* Monitor and manage controls and hygiene alerts to maintain compliance and operational excellence.
* Lead initiatives to promote best practices in infrastructure engineering and DevOps.
* Collaborate closely with SRE and production monitoring teams to ensure system reliability, performance, and rapid incident response.
Required Qualifications, Capabilities, and Skills
* Bachelor’s degree or higher in Computer Science, Engineering, or a related field, or equivalent formal training/certification.
* Proven hands-on experience with AWS services (Aurora/RDS, EKS, ECS, VPC, IAM, S3).
* Strong experience with Kubernetes (Amazon EKS).
* Proficiency with Infrastructure as Code (IaC) tools, such as Terraform.
* Experience automating operational tasks and CI/CD workflows.
* Demonstrated ability to design and operate resilient, scalable, and secure infrastructure in production environments.
* Excellent communication skills, with the ability to convey technical information clearly and build trust with stakeholders at all levels.
Preferred Qualifications, Capabilities, and Skills
* Practical experience deploying LLM-based applications into production and an understanding of MLOps.
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