Lead Data & AI Engineer at KLA Corporation

Architecting and deploying production AI/ML data pipelines and LLM-powered workflows, driving intelligent automation across business units. Leading cross-functional data engineering initiatives integrating Generative AI into enterprise data infrastructure. Building scalable Python backends and orchestrating multi-model AI systems in cloud environments.

Executive Snowflake Developer at KLA Corporation

Migrated 50+ legacy reports to Snowflake, writing and optimizing complex SQL queries and applying arithmetic transformations to ensure data accuracy and consistency across business units. Designed and deployed a Streamlit-based Python Data Analytics tool enabling self-serve reporting and interactive data exploration for non-technical stakeholders. Built and maintained Power BI dashboards consumed by 5+ internal Business Units, aligning KPI visualization with stakeholder requirements. Coordinated data requirements with internal BUs and stakeholders across 3 simultaneous projects in a team of 4, ensuring on-time delivery and cross-functional alignment.

Technology Program Analyst at Fiserv

Reduced application and Windows security vulnerabilities from ~6 million to ~3 million within one month through systematic patch management and remediation workflows. Managed enterprise access control in Active Directory, administering server and VDI environments to maintain security and compliance across the organization. Resolved user incidents and service requests end-to-end while supervising technology services delivery and supporting migration and troubleshooting activities. Partnered with application packaging teams to deploy organization-wide software, minimizing downtime and ensuring smooth user transitions.

Product Management Intern at India MART InterMESH Limited

Engineered a scalable LLM-based multi-label classifier using GPT-4, Gemini, and Mistral, automating sentiment, user-type, and two-level reason tagging from user feedback — reducing manual review time by 85%. Analyzed 12 months of historical user feedback (300,000+ records), optimizing data collection touchpoints and improving coverage by 40%, surfacing recurring platform pain points and UX gaps. Achieved 90%+ classification accuracy across diverse feedback types using a robust Python pipeline with multi-LLM ensemble evaluation and structured prompt engineering. Integrated a CrewAI-based multi-agent framework for parallel sentiment, reason, and user-type analysis, boosting processing efficiency by 60% and compressing feedback loop time from weeks to under 24 hours.