VP, Technology at Think Talent. Rebuilding talent assessment for the AI era.
15 years deep in leadership, talent, and culture assessment. The last few of those spent turning that domain into AI-native products — not bolting an LLM onto a legacy workflow, but reworking how assessment itself happens when models can read, reason, and respond.
I lead a engineering team across Spring Boot backends, cloud infrastructure, AI assessment pipelines, QA automation, and platform security. My role is systems architect and transformation driver — I work across the stack, but the real job is making sure what we ship actually changes how enterprise talent decisions get made.
- AI-native assessment — rethinking core Think Talent solutions from the ground up around what models can now do
- AI transformation for enterprise talent — packaging 15 years of assessment methodology and platform capability into deployment-ready offerings
- Delivery systems — onboarding, execution, and QA that let the team run end-to-end without me in the loop
Spring Boot · Java · cloud infra (AWS) · LLM orchestration and evaluation · QA automation · application security · enterprise data pipelines
Across BFSI, energy, manufacturing, and global consumer brands — enterprise-scale, high-stakes talent decisions.
I publish on the AI × Talent intersection on LinkedIn — practical, specific notes from someone actually building and deploying these systems at enterprise scale.
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