Ayesha Dar leads AI and ML engineering at Wolk Inc, working with startups and SMBs to ship machine learning systems that are reliable enough to run in production and measurable enough to justify the investment. She joined in 2023 after years in data engineering and applied ML at analytics firms in Islamabad and Toronto.

Her work spans the full ML lifecycle: data pipeline architecture, feature stores, model training infrastructure, MLOps tooling, monitoring, and inference systems. For clients building LLM-based applications, she designs RAG systems grounded in proprietary client data to reduce hallucination and improve auditability.

Ayesha is particularly focused on the gap between AI demos and AI in production. She has developed Wolk Inc's AI production readiness checklist — a 40-point framework covering data quality, model governance, inference reliability, and cost controls that every AI engagement is assessed against before launch.