Real-world Feedback Loops: Leverage multi-channel user feedback and real-world task data as primary research signals; design experiments and datasets to continuously improve agent and retrieval performance in production scenarios
Technical Leadership & Solution Ownership: Lead the technical direction of AI initiatives by driving architecture decisions, establishing best practices, mentoring team members, and guiding the end-to-end delivery of scalable AI solutions.
Collaborate and iterate cross-functionally: Work in an agile team with product managers, designers, and data scientists to turn requirements into well-designed APIs, and refine features iteratively as models and interfaces evolve.
Mentor junior AI engineers, set best practices for prompt engineering, RAG pipelines, and agent evaluation, and help build reusable AI infrastructure for the broader team.
Rapid Prototyping & Live Sprint Debugging: Execute real-time debugging and hotfixes on live agentic tools to keep the squad moving at maximum velocity.
Bachelor's degree or above in Computer Science or related fields, with 3+ years of engineering experience in at least one of Python, TypeScript, Java, or C/C++.