• Lead development teams to ensure sprint commitments are met • Define and maintain AI technical standards, reference architectures, prompt libraries, templates, and playbooks • Partner with sales and delivery to design AI strategies, architectures, and proof of concepts • Architect and deploy MLOps/AIOps platforms that support model lifecycle management, evaluation, and monitoring • Develop and deliver AI training programs, workshops, and internal documentation

• Serve as the technical leader, overseeing the entire lifecycle of Airbnb’s Kubernetes-based GPU platform. • Boost the efficiency and effectiveness of the Cloud Infrastructure organization by significantly enhancing all facets of the Machine Learning (ML) engineering experience. • Provide the reliability, scalability, security, and developer experience necessary for ML teams to deliver first class experiences to Airbnb Guests and Hosts around the world.

• Research, design, develop, and modify software systems including COBOL applications and batch jobs using JCL. • Present ideas for system enhancements and maintain knowledge of feature or technical changes within the application. • Transition workflows from legacy green-screens to modern tools such as Zowe and VS Code. • Conduct impact analysis for software changes and develop detailed technical designs and cost/task estimates. • Work closely with business analysts, designers, and subject matter experts to define requirements and technical solutions. • Produce detailed specifications, write software code, and develop interfaces or conversion routines leveraging APIs. • Conduct unit tests, document findings, and support formal testing. • Manage code migrations and adhere to program configuration change control standards and tools like Endevor CM and Gitlab. • Support the nightly batch cycles, participate in an on-call rotation, and provide daily system status updates.

• Research, design, and build AI-powered product prototypes across the Ekwa AI Labs portfolio, from idea to working implementation, owning technical and product execution end-to-end. • Rapidly experiment, iterate, and improve prototypes based on internal testing and defined success criteria. • Collaborate with internal stakeholders as needed. • Support founding and early client testing phases by refining product functionality, fixing issues, and improving usability. • Track learning velocity, prototype performance, and iteration outcomes to inform next steps and product decisions. • Document technical decisions, experiments, and learnings clearly to support future product development and scaling. • Research industry trends (dental, aesthetics, and broader healthcare) and integrate AI innovation best practices.