Most enterprise AI products fail not because the models are bad — but because no one connects what LLMs can do to what the business actually needs. That gap is where I operate — and it's how I drove $3M+ in incremental revenue and a 40% conversion lift across 50M+ annual customers at The Home Depot.
Experience
2024 — Now
2024 — Now
United States
Leading AI product strategy and development for enterprise-scale generative AI and agentic systems — serving 50M+ annual customers across 2,300+ stores. Cross-functional leadership of 12 (engineering, data science, UX) with C-suite visibility on AI strategy and governance.
🚀 Platform & Agentic AI
Architected and shipped enterprise multi-agent orchestration platform (GPT-4, Claude, Gemini) with RAG architecture and LangChain/LangGraph pipelines — cut merchandising decision latency by 25% for 2,000+ store operations teams
Engineered reusable AI platform infrastructure: prompt templates, LLM eval frameworks (RAGAS, BLEU), vector DB pipelines, model deployment on AWS SageMaker & Vertex AI, drift detection, and CI/CD for ML — accelerated feature development velocity by 30% across 5 teams
💰 Revenue Impact & Personalization
Spearheaded GenAI-powered personalized recommendation engine using collaborative filtering, embedding models, and semantic search — drove $3M+ incremental revenue and lifted conversion from 28% → 39% (+40%) across 50M+ annual customers in 6 months
Validated $500K annual inventory optimization via ML-driven promotional pricing through rigorous A/B experimentation
📊 Data-Driven Experimentation
Orchestrated A/B experimentation framework (power analysis, 95% confidence intervals) across 20+ quarterly tests — methodology adopted by 5 product teams
Conducted user research with 200+ store managers and merchandisers — translated insights into prioritized roadmap delivering 40% improvement in task completion time
Drove C-suite AI strategy visibility via 15+ KPI Power BI dashboards
🛡️ Responsible AI & Governance
Instituted responsible AI governance program: bias detection, SHAP explainability dashboards, human-in-the-loop validation, audit trails, safety guardrails, and model rollback protocols
Achieved 98% responsible AI compliance and reduced model rollback incidents by 60%
2020 — 2023
2020 — 2023
India
Led end-to-end product development for ML-powered enterprise financial technology platform serving 50,000+ users, balancing regulatory compliance (GDPR, MiFID II) with AI innovation across embedded ML models and real-time analytics.
📈 Product Launch & Impact
Launched enterprise Financial Portfolio Insights Platform with embedded ML models (ESG scoring, predictive risk analytics, asset optimization) to 50,000+ users
Improved client retention by 15% through AI-driven automated ESG scoring, personalized risk analytics, and predictive optimization
Designed Tableau dashboards visualizing portfolio performance, benchmark comparisons, and ESG metrics for real-time advisor decisions
🎯 Product Strategy & Roadmap
Owned product roadmap balancing regulatory requirements (GDPR, MiFID II), technical feasibility, and user needs
Drove feature prioritization through quantitative and qualitative research from 1,000+ customers using Qualtrics, SurveyMonkey, and usage analytics
Defined backlog for risk scoring, asset allocation, and portfolio rebalancing AI tools
⚡ Operational Excellence
Reduced release delays by 10% through proactive cross-functional risk management across data quality, compliance, and integrations
Led post-launch AI model monitoring and rapid incident response — accelerated resolution by 35% and reduced P1 incidents by 40%
Incorporated learnings from incidents into roadmap iterations for continuous improvement
Education
DePaul University