Senior AI Engineer

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September 27

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Sezzle

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Fintech • B2C • eCommerce

Sezzle is a financial technology company that offers a "buy now, pay later" service, allowing consumers to purchase products and pay for them in four interest-free installments over six weeks. The Sezzle app provides users with a flexible financing alternative to traditional credit cards, enabling instant approval decisions without impacting credit scores. Sezzle partners with various top brands, including Amazon, Walmart, and Target, to offer in-app and in-store payment options. The company's mission is to empower consumers financially by providing more financial freedom and control. It is available as a mobile app, with millions of downloads and high user ratings, and works towards accessibility and inclusion on its platform.

201 - 500 employees

Founded 2016

💳 Fintech

👥 B2C

🛍️ eCommerce

📋 Description

• Lead the design, development, and maintenance of scalable ML infrastructure on AWS, utilizing services like AWS SageMaker for model training and deployment. • Collaborate with product teams to develop MVPs for AI-driven features and enable rapid iteration and market testing. • Create and enhance monitoring and alerting frameworks to ensure high performance, reliability, and minimal downtime of ML models. • Enable cross-departmental use of AI/ML models, including Generative AI solutions, for various organizational use cases. • Provide production support: debug and resolve issues related to ML models in production and participate in on-call rotations. • Design and scale ML architecture to support rapid user growth, optimizing for robustness and cost-effectiveness. • Mentor team members, conduct code reviews, and elevate overall team capabilities through knowledge sharing and collaboration. • Stay updated with the latest advancements in machine learning technologies and AWS services and drive adoption of cutting-edge solutions.

🎯 Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, Statistics, Physics, or a relevant technical field, or equivalent practical experience. • At least 6+ years of experience in machine learning engineering, with demonstrated success in deploying scalable ML models in a production environment. • Deep expertise in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or related technical fields. • Proven track record of developing machine learning models from inception to business impact. • Proficiency with Python (required); experience with Golang is a plus. • Demonstrated technical leadership in guiding teams, owning end-to-end projects, and setting technical direction. • Experience working with relational databases, data warehouses, and using SQL to explore them. • Strong familiarity with AWS cloud services, especially AWS SageMaker, for deploying and scaling ML solutions. • Knowledge of Kubernetes, Docker, and CI/CD pipelines for efficient deployment and management of ML models. • Comfortable with monitoring and observability tools tailored for ML models (e.g., Prometheus, Grafana, AWS CloudWatch). • Experience developing recommender systems or enhancing user experiences through personalized recommendations. • Solid foundation in data processing and pipeline frameworks (e.g., Apache Spark, Kafka) for handling real-time data streams. • Willingness to provide production support and participate in on-call rotations for operational troubleshooting and incident resolution.