Our product, Personalization and Insights, builds and supports high throughput, low latency applications which leverage state of the art machine learning architectures, and which are deployed in AWS.  These applications power personalized experiences across Chase Consumer & 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.

As Applied AI ML Lead in Consumer and Community Banking, Personalization and Insights Team, you will build and 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. 

Job responsibilities

Deploy and maintain infrastructure (e.g., Sagemaker Notebooks) for providing an effective model development platform for data scientists and ML engineers that integrates with enterprise data ecosystem Build, deploy and maintain ingress/egress and feature generation pipelines to calculate input features for model training and inference Deploy and maintain infrastructure for batch and real-time model serving, in high throughput, low latency applications, at scale. Identify, deploy and maintain high quality model monitoring and observability toolsDeploy and maintain infrastructure for compute intensive tasks such as hyperparameter tuning and interpretability and explainabilityPartner with product, architecture, and other engineering teams to define scalable and performant technical solutions.       Leverage deep technical expertise to design extensible and scalable solutions, and to coach and grow individuals and teams.Ensure team executes work according to compliance standards, SLAs, and business requirements, to meet the objectives of an initiative.  Anticipates the needs of broader teams and potential dependencies with other teams. Identify and mitigate issues to execute a book of work while escalating issues as necessary.Proactively helps maintain high operational excellence standards for our production systems.  Encourages development of technological methods and techniques within team.

Required qualifications, capabilities, and skills

BS degree in Computer Science or related Engineering field7+ years applied experienceDeep experience and passion in model training, build, deployment and execution ecosystem such as Sagemaker and/or Vertex AI Experience in monitoring and observability tools to monitor model input/output and features statsOperational experience in big data tools such as Spark, EMR, RayExperience and interest in ML model architectures—linear/logistic regression, Gradient Boosted Trees, Neural Network architecturesSolid grounding in engineering fundamentals and analytical mindset. Bias for action and iterative developmentProgramming languages: Python, some JavaSolid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc.]Solid fundamentals and experience with cloud technologies—EC2, Sagemaker, IAM. Good knowledge of Databases

 

Preferred qualifications, capabilities, and skills

Experience with recommendation and personalization systems is a plus.