Gokul Gawande

About Gokul Gawande

AI is advancing faster than our ability to trust it.Most organizations are not struggling with building AI they are struggling with making it reliable, accountable, and safe. That’s the space me and my team work in. With 12+ years across data engineering, analytics, and model validation, I focus on building AI systems that are not only high-performing, but also trustworthy, explainable, and aligned with governance standards.In many cases, the real risk isn’t model failure but unvalidated outcomes. Without strong data quality checks, bias controls, and governance frameworks, AI systems can create silent failures at scale. Areas I work on: • Data quality and validation in AI pipelines • Responsible AI (RAI) and governance frameworks • Algorithmic auditing, bias detection, and explainability practices • Trustworthy AI assessments (including Z-Inspection®) At Roche’s Diagnostics Data Analytics & Research division, I lead a group data engineers focused on building analytics products, and validation engineers that translate ethical AI principles into operational frameworks supporting real-world healthcare use cases. My experience spans telecommunications, reinsurance, and healthcare industries where data-driven decisions carry real consequences. It’s shaped how I think about AI: not just in terms of performance, but dependability.I’ve also co-authored paper on assessing the trustworthiness of Generative AI, bringing practical perspectives to an evolving field.

Experience

1. Engineering Manager Ivd Data Insights

   Roche

   Apr 2025 — Present · Pune, IN

Education

• Sant Gadge Baba Amravati University, Amravati

  Bachelor of Engineering (BE), Information Technology

  2011 — 2014

• Dr. D. Y. Patil Vidyapeeth

  Master of Business Administration - MBA, Artificial Intelligence & Machine Learning