At CDAO (Chief Data Analytics Office), we drive our firm’s strategic investments in AI/ML and data-oriented tools and capabilities. Our engineering team is at the forefront of building innovative platforms, automating infrastructure operations, and enabling Agentic-based AIOps platforms. Our mission is to enhance scalability, security, and reliability for CDAO-hosted managed services.
As a Senior Lead Software Engineer at JPMorgan Chase within CDAO you will play a crucial role in an agile team, focusing on the enhancement, construction, and delivery of trusted, market-leading technology products in a secure, stable, and scalable manner. Your skills and contributions will have a significant impact on the business, and your deep technical expertise and problem-solving methodologies will be applied to address a wide range of challenges across various technologies and applications.
Job responsibilities
Designs and implements scalable cloud native software solutions using modern technology stacks to deliver highly available, performant, and resilient products.Develops secure and high-quality production code, and reviews and debugs code written by others.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 CycleInfluences peers and project decision-makers to consider the use and application of leading-edge technologies.
Required qualifications, capabilities, and skills.
Formal training or certification on software engineering concepts and 5+ years applied experience.Advanced in one or more programming language(s)(Eg: Java, Python).AWS or EKS or Terraform Certifications.Knowledge and deep understanding of AWS concepts, including Athena/Glue/EMR/S3/SQS/SNS/Lambda etc.Proficient in AWS, EKS/ECS, Java/Python/Microservices based applications and Spring Boot.Deep experience in Java or Python development along with leveraging Terraform to build infrastructure in AWS.Hands-on experience implementing DevOps practices using tools such as Docker, Jenkins, Spinnaker, and Terraform.Hand-on experience with coding and testing microservices.Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.
Preferred qualifications, capabilities, and skills
Understanding and hands-on experience with public cloud technologies, especially with AWS, in the context of ML engineering workflows, specifically featurization, experimentation, training, and evaluation (Sagemaker/EMR)