- Translate model architectures, data analysis, and feature engineering into scalable agent design patterns.
- Assess legacy workflows and design automated, AI-driven alternatives.
- Structure data, operational constraints, and guidelines to improve LLM performance and output reliability.
- Develop low-fidelity prototypes to validate AI features, user experiences, and model feasibility.
- Design business-aligned evaluation frameworks, metrics, and data collection to measure program impact.
- Architect and deploy AI agents, including RAG solutions, vector search integration, and multi-agent systems using A2A and MCP.
- Present and communicate technical work to connect execution with business strategy for stakeholders.
Product ManagementSoftware Engineering