I turn enterprise complexity into reusable product systems.
Platform PM with experience across JPMorgan Chase and Yum! Brands, building multi-tenant onboarding, governed data products, and practical AI tooling. I specialize in reducing operational friction through reusable contracts, workflow orchestration, and measurable product leverage.
18 engineers + 4 consultants led · 15 payment integrations · 68 distributed teams unified
Scaled on a high-throughput multi-tenant digital onboarding engine.
Generative AI authoring shifted config rights safely to operations.
Core API schemas & upfront validation logic redesigned cleanly.
Reduced from a 72-hour manual baseline across digital banking STP.
Platform Product Leadership
I reduce operational friction by building reusable systems — multi-tenant architecture, API contracts, governed data access, and workflow orchestration — so teams can move faster without multiplying engineering work.
Multi-Tenant Architecture
Designing reusable product systems that separate shared platform logic from client-specific configuration, making scale easier without multiplying engineering work.
Contracts & Orchestration
Defining product contracts, validation logic, and routing rules that reduce handoffs, improve reliability, and shorten time-to-completion in complex workflows.
Governed Data Access
Building trusted data products with fine-grained RBAC/IAM controls, so analytics and ML teams can self-serve without sacrificing enterprise governance.
Telemetry & Reliability
Using continuous instrumentation and experiments to pinpoint user friction, improve platform adoption, and drastically cut time-to-detect when critical workflows fail.
How I Build Products
Core operating principles forged across enterprise platforms, mission-critical checkout funnels, and data infrastructure.
Start with the operating constraint, not the feature request.
Diagnose upstream friction, system boundaries, and handoffs before committing engineering capacity.
Standardize where reuse compounds; preserve flexibility where customer needs differ.
Separate shared platform contracts from isolated line-of-business runtime configuration.
Use AI where probabilistic systems create leverage; keep deterministic controls where risk demands certainty.
Confine generative workflows to draft authoring; enforce hard deterministic validation and review gates.
Measure platform success through adoption, reliability, developer leverage, and business outcomes.
Prove platform leverage when squads accelerate delivery without multiplying engineering overhead.
Flagship Product Case Studies
Three deep dives showing technical product judgment under constraints — detailing the operating context, strategic architectural tradeoffs, execution decisions, and verified outcomes.
Enterprise onboarding was fragmented across 68 distributed teams with repeated handoffs, causing 72h–1 week processing latencies and costly redundant builds across lines of business.
Owned platform roadmap and schema model across 15 integrations; led cross-functional delivery with 18 engineers and 4 consultants.
- Scaled platform from 45 to 180+ enterprise accounts across 15 integrations without proportional team growth.
- 72h → <1h Processing Latency
- 90% Lower Integration Error Rate
- 80% Fewer Support Tickets via GenAI Config
Fast-Track Onboarding Straight-Through Processing (STP)
Corporate banking intake & onboarding platform spanning web and mobile for middle-market clients, orchestrating Wires, ACH, RTP, and account verification services.
Non-engineers faced backlogged manual tickets to configure complex onboarding logic. Designed a GenAI authoring flow converting specs into draft platform schemas.
Confined AI strictly to draft configuration with deterministic validation gates and human approval—cutting configuration tickets 80% with all generated configurations passing validation and human review before deployment.
Model outputs evaluated against schema contracts with deterministic policy gates. Low-confidence outputs route to manual review with zero direct write access to financial limits.
Full AI extraction & auto-routing payload; risk of non-deterministic financial limit calculation and compliance breach.
Confined AI strictly to draft configuration with deterministic validation gates and human approval—cutting configuration tickets 80% with all generated configurations passing validation and human review before deployment.
Model outputs were evaluated against predefined acceptance thresholds, with low-confidence or policy-sensitive outputs routed to deterministic workflows or manual review. Human-in-the-loop approvals, RBAC, immutable audit logging, and hard decision boundaries prohibiting model approval of financial limits or schema mutations.
I would establish formal schema governance and cross-squad platform adoption metrics earlier in the rollout phase, once reusable workflow adoption became the primary indicator of platform leverage over pure processing time.
Cart drop-offs and authorization timeouts across KFC, Pizza Hut, and Taco Bell were hard to isolate due to fragmented logging across heterogeneous mobile and web platforms.
Owned checkout funnel strategy, telemetry strategy and instrumentation requirements, and gateway failover rules; prioritized instrumentation over rewrites across engineering squads and vendors.
- 85% Reduction in MTTD
- Conversion Lift across 50M+ Transactions
- Surgical Fix in Weeks vs. 6-Month Rebuild
- Global Observability Standard Deployed
Checkout Funnel Optimization & Payment Gateway Resilience
High-volume consumer checkout platform serving KFC, Pizza Hut, and Taco Bell globally across web and native mobile apps with active/active gateway failover.
Complete front-to-back rebuild advocated by engineering ($M capital expenditure, 6+ month feature freeze).
Instrumented granular telemetry by device, browser, and gateway. Analysis showed roughly 60% of affected checkout failures were concentrated in two gateway-browser combinations, allowing us to solve the issue with targeted dynamic retry flows rather than an expensive multi-quarter rewrite.
Tradeoff & Consequence: Solved root cause via targeted retry logic, avoiding an unneeded rebuild and saving months of roadmap time while safeguarding checkout conversion across 50M+ transactions.
I would introduce continuous synthetic multi-tender probe transactions in pre-production earlier, rather than relying primarily on live telemetry anomaly detection across production traffic.
Sales pipeline figures lived in isolated CRM instances and manual spreadsheets. Executives had no unified pipeline visibility and ML models had untrusted data inputs.
Owned data product roadmap, contract standards, and RBAC security model; prioritized reliability and governance over speculative UI requests with 10+ sales leaders.
- +35% Lift in Daily Active Usage (DAU)
- Automated Hourly Feeds (Replaced Manual Excel)
- RBAC Blueprint Adopted Across 2 JPMC Platforms
- Automated Anomaly Detection & Lineage
Payments Sales Analytics Data Platform (CRM → Redshift)
Enterprise Payments Sales data lakehouse consolidating commercial pipeline health, deal stages, and conversion forecasting across multiple global Lines of Business.
Fast delivery of 3 bespoke visualization dashboards requested by sales leaders without underlying reconciliation.
Prioritize automated anomaly detection, row-level lineage tracking, and multi-tenant RBAC enforcement.
Tradeoff & Consequence: Usage telemetry demonstrated that users reverted to Excel because numbers didn't match. Improved daily active usage 35% after prioritizing data reliability, lineage, and anomaly detection over additional dashboard features.
I would introduce formal upstream data-contract SLAs earlier in the scaling phase, once schema drift became a recurring cross-team dependency rather than treating it as an ingestion-level issue.
Core Competencies & Technical Skills
Bridging executive product strategy, multi-tenant enterprise system architecture, and hands-on technical execution.
Platform Product Management
- checkProduct Strategy & Executive Roadmaps
- checkMulti-Tenant Enterprise Platforms
- checkAPI Contract Design & Schema Governance
- checkWorkflow Orchestration & Integration
AI Systems Design
- checkAgentic AI Workflows & State Machines
- checkLLM Tool-Calling & Structured Output
- checkRetrieval-Augmented Generation (RAG)
- checkEvaluation Frameworks & Guardrails
Data & Growth Analytics
- checkFunnel Telemetry & Error Observability
- checkMulti-Variant A/B Testing & Experiments
- checkData Pipelines & Lakehouse Governance
- checkAdoption, DAU & Reliability Metrics
Where I Have Led Product Work
A high-velocity advancement track across platform engineering, data governance, and AI systems — with hands-on technical literacy as the core operational differentiator.
Lead product strategy for a multi-tenant onboarding platform serving 180+ enterprise accounts across 15 complex integrations.
- arrow_rightScaled platform: from 45 to 180+ enterprise accounts across 15 integrations by directing a cross-functional squad of 18 engineers and 4 consultants.
- arrow_rightReduced engineering support tickets 80%: by launching a generative AI authoring tool with human-in-the-loop approvals, role-based access, audit trails, validation guardrails, and ongoing accuracy monitoring.
- arrow_rightReworked core APIs and validation workflows: bringing processing time down from 72 hours to under 1 hour and reducing integration errors by 90%.
- arrow_rightGrew adoption to 1,500+ users: through phased rollouts, product experiments, usage analysis, and quarterly roadmap reviews with senior leaders; the results helped secure funding for three release cycles.
- arrow_rightIntroduced reusable schemas and workflows: that 68 teams could configure once and use across products, avoiding duplicate engineering work.
Led the roadmap for a cloud data platform that replaced fragmented, manual reporting.
- arrow_rightIncreased daily active usage 35%: by shifting the roadmap toward data reliability after user feedback and usage data showed that trust not missing features was the main adoption barrier.
- arrow_rightBrought data into Amazon Redshift: from multiple source systems, giving sales and analytics teams one reliable place to access high-volume enterprise data.
- arrow_rightCreated role-based access and row-level security standards: that were later adopted by two other platform teams.
- arrow_rightWorked with 10+ sales leaders and data science partners: to turn reporting and access needs into clear product requirements for ingestion, modeling, governance, and permissions.
Owned the checkout experience for KFC, Pizza Hut, and Taco Bell digital ordering platforms, supporting 50M+ annual transactions.
- arrow_rightReduced Mean Time to Detection (MTTD) 85%: for payment gateway anomalies by partnering with engineering to build a real-time diagnostic dashboard.
- arrow_rightReduced checkout abandonment: and informed the release roadmap through systematic A/B tests on error handling and retry experiences.
- arrow_rightAdded end-to-end tracking: across web and mobile to identify where latency and errors caused customers to abandon checkout across 50M+ annual transactions.
Constructed predictive lead-scoring models and automated big-data ETL pipelines (PySpark, SQL, Airflow) supporting nationwide fiber expansion.
- arrow_rightReduced reporting time 30%: for regional sales teams by building predictive lead-scoring models and automated ETL pipelines using SQL, PySpark, and Airflow.
- arrow_rightMoved 100+ city launch teams from spreadsheets: to standardized reporting by designing KPI scorecards for onboarding, upgrades, and activation across a 1,600+ town rollout.
- arrow_rightIdentified high-potential markets: by analyzing registration and trial-conversion signals, improving sales prioritization, and local launch decisions.
- arrow_rightWorked with cross-functional teams: partnering with sales, network operations, and product teams to turn broadband usage and service-adoption data into practical launch recommendations.
- arrow_rightImproved CRM stage hygiene and forecast accuracy: by supporting TAM/SAM sizing and territory planning and building weekly pipeline views in Excel and Power BI.
- arrow_rightStrengthened sales messaging: by producing win/loss analyses, battlecards, and ROI inputs for the regional sales team.
school Academic Background
Executive MBA
University of the Cumberlands
MS Business Analytics
University of Louisville
B.Tech Computer Science
Bharath University
Solo Builds & Agentic Benchmarks
Hands-on technical builds used to stress-test model behavior, agentic state machines, and AI governance guardrails outside enterprise environments. Kept separate to ensure clear architectural scale.
Feedback-to-Backlog AI Copilot
AI pipeline converting unstructured customer feedback (support tickets, calls, app reviews) into an explainable, ranked product backlog with deterministic fallback and Jira REST sync.
- check 100% extraction accuracy on 40-ticket set
- check Layered RapidFuzz + Gemini semantic dedup
- check Explainable drag-to-reorder RICE scoring
ApexApply — Agent Benchmarking
Agentic runtime environment evaluating token economy, tool-calling latency, and state machine robustness when reasoning over dynamic web interfaces without fragile CSS selectors.
- check Multi-model: Gemini vs Ollama vs xAI
- check Intent classification without fixed CSS IDs
- check Persistent multi-stage memory retention
OKF ContextGate Trust Gateway
Deterministic governance proxy based on Google's Open Knowledge Format (OKF). Decides what an LLM is allowed to ingest before it formulates answers, with tamper-evident audit receipts.
- check Deterministic: ALLOW / WARN / BLOCK / ESCALATE
- check Tested across 7 adversarial failure models
- check 100% audit trail receipts written to DuckDB
Verified Certifications
Verifiable credentials in cloud infrastructure, platform governance, customer analytics, and product management.
Foundations of Project Management
Customer Analytics
Joint Certificate in Data Analytics
AWS Certified Solutions Architect – Associate
Open to Senior Platform & AI PM Opportunities
Interested in multi-tenant architectures, AI workflow automation, data lakehouse platforms, and high-leverage product systems. Available for select executive discussions and senior product leadership roles.