Sezzle is looking for an AI Engineer I to join our Applied AI team. In this role, you'll contribute to the platforms and systems that power Sezzle's AI-driven capabilities. You'll build AI-powered features, intelligent automation, and agentic systems, embedding AI into both customer-facing products and internal workflows. This is an ideal role for engineers early in their AI careers seeking hands-on exposure to production-grade AI systems.

What You'll Do

Contribute to the design and development of AI-powered platforms and systems.
Own end-to-end delivery of complex projects from problem discovery through production rollout.
Build AI-driven features, intelligent automation, and agentic systems.
Embed AI capabilities into existing team workflows, products, and processes.
Integrate AI capabilities into customer-facing products, internal tools, and operational workflows.
Build full-stack components spanning backend services, APIs, data layers, and frontend interfaces.
Translate business and operational problems into maintainable, AI-enabled technical solutions.
Apply systems thinking to ensure reliability, performance, and security.
Collaborate with engineers, teams, and stakeholders across the company.
Participate in production support and incident response for team-owned systems.
Engage in code reviews and knowledge sharing.

What We're Looking For

1–3 years professional experience in software engineering, AI engineering, platform engineering, or related roles.
Hands-on experience designing, building, and shipping AI-powered systems into production.
Strong proficiency in Python, SQL, and frontend frameworks such as React.
Proven experience designing and owning production-grade full-stack systems.
Experience working with distributed systems, internal platforms, or complex data workflows.
Ability to operate effectively in ambiguous problem spaces and drive solutions with minimal direction.

Nice to Have

Exposure to building AI-powered platforms or products, including contributing to architectural decisions.
Foundational knowledge of core machine learning and data science principles.
Familiarity with transformer architectures (BERT, GPT, etc.) and basic understanding of attention mechanisms, tokenization, and fine-tuning.
Exposure to embeddings and vector representations; interest in vector databases.
Familiarity with NLP techniques such as text classification, named entity recognition, or semantic search.
Familiarity with real-world agentic frameworks or workflow engines.
Experience developing AI-assisted or agentic systems that interact with tools, data, or workflows.
Familiarity with cloud-native development using AWS.
Experience with containerized systems and orchestration tools such as Docker and Kubernetes.
Exposure to CI/CD pipelines and modern development workflows.
Experience with Golang is a plus (or willingness to learn).