We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As an Applied AI ML Lead within the Consumer & Community Banking, Digital Intelligence team, you will collaborate with all lines of business and functions to deliver software solutions. You will have the opportunity to experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a significant impact on technology and business. You will design and implement highly scalable and reliable data processing pipelines, perform analysis and insights to promote and optimize business results. 

Job Responsibilities:

Deploy and maintain infrastructure 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 tools Deploy and maintain infrastructure for compute intensive tasks such as hyper-parameter tuning and interpretability and explain ability Partners with product, architecture, and other engineering teams to define scalable and performant technical solutions       Leverages deep technical expertise to design extensible and scalable solutions, and to coach and grow individuals and teams Ensures 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  Identifies and mitigates 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 Creates a culture of diversity, equity, inclusion, and respect for team members and prioritizes diverse representation

 Required qualifications, capabilities and skills: 

Formal training or certification on software engineering concepts and 5+ years applied experience Deep 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 stats Operational experience in big data tools such as Spark, EMR, Ray Experience and interest in ML model architectures - linear/logistic regression, Gradient Boosted Trees, Neural Network architectures Solid grounding in engineering fundamentals and analytical mindset Experience in taking initiative and embracing iterative development

 

Preferred qualifications, capabilities and skills :

Experience with recommendation and personalization systems Programming languages: Python, some Java Experience in containers (docker ecosystem), container orchestration systems (Kubernetes, ECS), DAG orchestration (Airflow, Kubeflow etc.) Experience with cloud technologies—EC2, Sagemaker, IAM