Senior AI Engineer

Role Overview

• Oversee the design, development, and deployment of machine learning models that enhance our financial platform.
• Drive the creation of scalable machine learning solutions for personalized recommendations in the Sezzle marketplace, fraud detection, and credit risk assessment.
• Collaborate with a team of engineers and data scientists to build large-scale, high-quality solutions that address challenges in the shopping and fintech space.
• Design and build scalable machine learning infrastructure on AWS, utilizing services like AWS Sagemaker.
• Work closely with product teams to develop MVPs for AI-driven features, ensuring quick iterations and market testing.
• Create and enhance monitoring and alerting systems for machine learning models.
• Enable various departments to leverage AI/ML models, including Generative AI solutions, for different use cases.
• Provide expertise in debugging and resolving issues related to machine learning models in production, participating in on-call rotations.
• Mentor team members through knowledge sharing and collaboration.

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, demonstrating the ability to solve complex challenges with innovative solutions.
• Proficiency with Python is required, and experience with Golang is a plus.
• Demonstrated technical leadership in guiding teams, owning end-to-end projects, and setting the technical direction to achieve project goals efficiently.
• Experience working with relational databases, data warehouses, and using SQL to explore them.
• Strong familiarity with AWS cloud services, especially in deploying and managing machine learning solutions and scaling them in a cost-effective manner.
• Knowledgeable in Kubernetes, Docker, and CI/CD pipelines for efficient deployment and management of ML models.
• Comfortable with monitoring and observability tools tailored for machine learning models (e.g., Prometheus, Grafana, AWS CloudWatch) and experienced in 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.

Tech Stack

• Apache
• AWS
• Cloud
• Docker
• Grafana
• Kafka
• Kubernetes
• Prometheus
• Python
• Spark
• SQL
• Go