About the position
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.
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 Cycle.
- Influences peers and project decision-makers to consider the use and application of leading-edge technologies.
Requirements
- 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.
- Hands-on experience with coding and testing microservices.
- Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.
Nice-to-haves
- 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)
