AI Engineer II

Sezzle is seeking a highly skilled and motivated AI Engineer II to join our dynamic, fast-paced Applied AI team. This role offers significant opportunities for growth and increased ownership as you take on end-to-end responsibility for complex AI-driven platforms and systems. You will build AI platforms from scratch, develop intelligent automation and orchestration systems, and integrate proprietary AI solutions into customer-facing products, internal tools, and operational workflows. Operating in ambiguous problem spaces, you will collaborate with product, engineering, and operations teams to identify and implement AI solutions that enhance customer experience, improve decision-making, and automate complex workflows. This is an opportunity for engineers ready to lead solutions, shape architecture, drive implementation, and ensure the reliability, scalability, and usefulness of our AI-enabled systems.

Key responsibilities include leading the design, development, and ownership of AI-powered internal platforms and systems, owning end-to-end project delivery from discovery to production, designing and implementing AI-driven features and automation, embedding AI capabilities into products and workflows, building full-stack systems (backend, APIs, data, frontend), translating business problems into AI solutions, applying systems thinking for reliability and security, collaborating with stakeholders, participating in production support, and mentoring AI team members.

We are looking for candidates with 3-5 years of professional experience in software engineering, AI engineering, or platform engineering, with hands-on experience building and shipping AI-powered systems to production. Strong proficiency in Python, SQL, and frontend frameworks like React is required, along with proven experience designing and owning production-grade full-stack systems and experience with distributed systems or complex data workflows. The ability to drive solutions in ambiguous environments with minimal direction is essential.

Preferred qualifications include experience building AI-first platforms from scratch, a strong foundation in machine learning and data science principles, experience with transformer architectures (BERT, GPT), developing AI-assisted or agentic systems, familiarity with embeddings and vector databases, NLP techniques, AWS, prompt engineering, RAG, LLM application patterns, containerization (Docker, Kubernetes), CI/CD pipelines, and Golang (or willingness to learn).

Ideal candidates possess relentlessly high standards, are not bound by convention, value speed and calculated risk-taking, and earn trust through attentive listening, candid communication, and respectful treatment of others.