Jasmine Omeke
Jasmine Omeke is a senior software engineer at Airbnb, where she focuses on data infrastructure. Before joining Airbnb, she worked as a software engineer at Netflix and PayPal, specializing in distributed systems and large-scale data processing, and building backend services that handled petabytes of data. She has experience creating eLearning content and authored a Python testing course on LinkedIn Learning. Jasmine also mentors aspiring computer science students through CodePath’s technical interview preparation program. A former Gates Millennium Scholar, she is excited to give back to the community that supported her early academic journey. Outside of work, Jasmine enjoys swimming and sewing. She holds a Bachelor of Arts from Harvard University and a Master of Computer Science from DePaul University.
Session
AI tooling is moving fast, but many Python developers are unsure where to start or how today’s AI patterns fit into systems they already know how to build. This talk is a practical, hands-on overview of modern AI development patterns in Python, focused on what you need to know to go from zero to hero.
We’ll walk through a real-world coding example broken into parts that illustrate the core building blocks of modern AI applications, and explain when each pattern makes sense. This example is designed in a way that doesn’t require any prior machine learning experience, and attendees will leave with an understanding of how AI systems work, what problems they’re good at solving, and how to maintain and observe what has been built.
Topics we’ll cover:
The modern AI stack in Python: LLM APIs, embeddings, tools, and agents
Common Python AI patterns: prompts, function calling, RAG, and simple agents
When to use a script vs an agent vs a service (and when not to)
How to get something working quickly without sacrificing reliability or safety
Practical guardrails: handling errors, controlling outputs, and protecting data
How to generally stand up common AI workflows, such as LLM-powered scripts to lightweight AI agents / MCP-style services.
Attendees will leave with a clear map of the AI landscape, working Python patterns they can reuse immediately, and the confidence to start building AI features without needing a machine learning background.