About the position
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products. As a Senior Lead Software Engineer at JPMorgan Chase within the Consumer and Community Banking's Personalization and Insights group, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. Our product, Personalization and Insights, builds and supports high throughput, low latency applications which leverage state of the art, machine learning models hosted on AWS. These applications power personalized experiences across Chase Consumer and Community Banking channels, to help weave a user experience that includes traditional banking services with other services in the Travel, Merchant Offer Shopping, and Dining spaces. In this role, you'll define, build and evolve the infrastructure required to run batch and real time models, and to maintain pipelines for model training, batch/real-time model serving, hyperparameter tuning at scale, model monitoring, production validation and other activities vital for model development, testing and deployment in a well-managed, controlled environment.
Responsibilities
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Drives decisions that influence the product design, application functionality, and technical operations and processes
- 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
- Adds to the team culture of diversity, opportunity, inclusion, and respect
Requirements
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s): Python and Java
- Experience and passion in model training, build, deployment and execution ecosystem such as Sagemaker and MLOps libraries such as Ray is needed
- Experience in monitoring and observability tools to monitor model input/output and features stats
- Experience and interest in ML model architectures-linear/logistic regression, Gradient Boosted Trees, Neural Network architectures
- Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Ability to tackle design and functionality problems independently with little to no oversight
- Experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc] is required
- Experience with cloud technologies like EC2, Sagemaker, IAM is required
- Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field
Nice-to-haves
- Experience building high-throughput, low-latency micro service development leveraging AWS services such EKS, ECS, Fargate, etc.
- Hands-on experience with public cloud systems - AWS preferred
- Experience with recommendation and personalization systems
- Developing software in a well-managed SW dev environment such as Banking
- Good knowledge of database
