Solutions Engineer specialized in delivering high density technical output and bridging the gap between complex engineering and business objectives. Proven track record of working as a developer, architect, and project lead to eliminate friction and accelerate time-to-market. Expert in architecting deterministic AI modules, identifying high-value KPIs for businesses like Microsoft and Advance. Accelerated dual-degree graduate (CS & ITWS) with a methodology focused on rapid adaptability and organizational impact.
Architected a centralized enterprise dashboard to aggregate fragmented operational telemetry, establishing a single source of truth for executive KPI monitoring and strategic decision-making.
Engineered data ingestion pipelines integrating disparate third-party APIs and asynchronous file uploads into a normalized relational schema.
Produced the deployment architecture roadmap for full legacy system modernization, bridging existing infrastructure to new tooling with minimal operational disruption.
The objective was organizational modernization: consolidating fragmented operational data into a single source of truth for executive decision-making. Data ingestion pipelines were engineered to integrate disparate third-party APIs and asynchronous file uploads into a normalized relational schema, with a centralized enterprise dashboard architected on that foundation for real-time executive KPI monitoring. Authentication and authorization were integrated into existing security frameworks without disrupting current operations, and the full deployment architecture was mapped to give leadership the visibility required for infrastructure-level decisions.
Microsoft (External Consulting Engagement) • Remote / Troy, NY
Owned the Microsoft Teams Early-Career Experience Enhancement project end-to-end — a retention problem with no predefined architecture, a three-month delivery window, and scope spanning qualitative field research through executive financial modeling.
Designed the research methodology, conducted qualitative user studies, and benchmarked against Slack and Discord to identify differentiation opportunities, producing the Action Feed System specification and a validated high-fidelity Figma prototype.
Produced a CBA, Risk Assessment and Mitigation Plan, PRD, and research findings to fully equip future implementation teams.
Product StrategyUX ResearchFigmaCompetitive AnalysisFinancial ModelingPRD Development
AI Developer
May 2025 — Aug 2025
Advance Carts • Boca Raton, FL
Led the development of a proprietary deterministic AI model to improve internal search efficiency, prioritizing reliability and auditability over probabilistic generation.
Researched employee workflows to isolate operational bottlenecks, containerizing solutions via Docker while defining deployment architecture and processes.
Audited and refactored legacy code for compatibility with current system architecture, reducing integration friction across the stack.
The core deliverable was a proprietary deterministic AI model for internal search optimization, with reliability and auditability as hard constraints rather than design preferences. Before writing the model, employee workflows were audited directly to isolate the operational bottlenecks the system needed to solve. The solution was containerized for deployment consistency, and legacy codebases were refactored for compatibility before integration, eliminating blockers before they reached the production stage.
Architected high-fidelity data visualization dashboards to map logistics networks, translating raw tracking data into actionable optimization insights.
Applied algorithmic analysis to identify route optimizations that directly reduced per-route transportation costs.
Surfaced previously unused route availability, expanding the operational network and broadening logistics capacity.
The mandate at Prosper Logistics was translating raw logistics tracking data into decisions that affected the bottom line directly. High-fidelity visualization dashboards mapped the full network, converting abstract tracking metrics into actionable operational intelligence. Algorithmic analysis applied to those maps identified route optimizations that reduced per-route transportation costs and surfaced previously unused route availability, expanding network capacity without adding infrastructure.
Business IntelligenceData VisualizationAlgorithm DesignLogistics AnalyticsSQL
Artificial Intelligence Researcher
May 2024 — Dec 2024
Telos Athletics • Remote
Conducted dataset research and prototyped predictive performance models to support athlete talent identification pipelines.
Isolated key predictive features that measurably improved model accuracy for athlete performance forecasting.
Containerized the model environment using Docker to guarantee reproducibility across development and production machines.
At Telos Athletics, talent acquisition decisions were being made without a predictive model grounded in performance data. Dataset research and feature engineering isolated the athletic attributes with the highest predictive signal, forming the basis of prototyped performance models for athlete identification pipelines. The model environment was containerized in Docker to enforce reproducibility across development machines, making reliability a property of the system rather than an assumption about the environment.
Architected and normalized relational databases to ensure high-quality data retrieval, establishing a foundational single source of truth for analytics.
Migrated and aligned legacy schemas to updated database standards, preserving data integrity across all integrated systems.
Built dynamic data visualizations with automatic refresh on new data ingestion, enabling real-time reporting.
The work started at the data layer. Fragmented legacy schemas had no unified retrieval standard, so the initial priority was normalization: relational databases redesigned as a single source of truth, with legacy structures migrated into updated frameworks without data loss. From that clean foundation, dynamic visualizations were built to refresh automatically on new ingestion, converting the normalized store into live reporting infrastructure rather than a static archive.