• Built the AI-powered RPS Insights framework that generates real-time recommendations for global datacenter build planning, reducing planner analysis cycles and improving decision accuracy. 

• Developed the Network Planner Assessment Tool using OpenAI to automatically evaluate regional and parcel impact scenarios, cutting review time from hours to minutes. 

• Engineered real-time synchronization between SOM (Signal Order Management) and RPS across 50+ order types, reducing order proposal workflows by 75% and eliminating system-switching for planners. 

• Delivered core components of the OneMDM → RPS migration by implementing metro-specific business rules, context-aware UI behaviors, and fault-tolerant data integration. 

• Redesigned the RND Layout Engine, decoupling rendering components and resolving long-standing layout failures impacting 50+ planners. 

• Led the EV2 migration for RPS Order Management, removing all S360 security blockers, enforcing modern TLS compliance, and restoring deployment velocity. 

• Developed reliable multi-region data synchronization pathways across SOM, CSPW, and RPS, reducing data integrity issues across planning workflows.

Led strategic initiatives in software engineering, driving AI-powered insights integration, automated testing solutions, and optimized architecture for Microsoft's Azure for Operators insights platform.

Key Accomplishments:​

• Created an AI Insights Python framework for mobile network testing, analyzing logs, time-series data, call model traces, and performing anomaly detection & core dump analysis. Utilized sentiment analysis and GPT4 for log analysis, working closely with Microsoft Research, drastically reducing bug detection and RCA time from 2 hours to 5 minutes.

• Developed auto-ticketing and duplicate incident tracking system using OpenAI LLMs and sentence embeddings, enabling automatic RCA identification and resolution for new incidents, enhancing issue detection for major customers such as AT&T, Etisalat, 3UK, decreasing incident time and contributed to a projected $4.6 million in savings.

• Developed end-to-end smoke, soak, and resiliency Python test suites via Azure DevOps Pipelines, adopted by over 5 teams, achieving a 95% reduction in manual testing hours and saving approximately 110 hours monthly

• Led major infrastructure development and architecture refactors in Azure, deploying multiple production-grade Kubernetes clusters with Azure Key Vault, Application Gateway, enhancing security and deployment efficiency.

• Conducted Azure GPT training for 143 developers, contributed to multiple AI-related patents, authored a white paper on Iterative Testing, and led tech sessions on AI and Machine Learning fundamentals.

• Patents Filed: AI-BASED ROOT CAUSE ANALYSIS FOR TELECOMMUNICATIONS SYSTEMS & SMART PROMPT GENERATOR FOR GPT MODELS

Working in an Agile train environment to develop and deliver microservices for CVS Health clients. Engaged in the identity access management train and played a key development role in year-long authentication platform migration.

• Lead developer on identity access management microservice used to authenticate users for a major CVS Health client

• Developed additions to existing automated testing application to support next generation authentication methods

• Worked closely with system architecture team to integrate performance optimizations into end-to-end systems

• Mentor and leader for several members onboarding onto the team, providing in-depth knowledge transfers to peers

• Lead developer on company’s primary video analytics tool that allows fellow developers to monitor camera performance, view realtime detection statistics, and add actionable information to cameras

• Created, trained, and tested one of two machine learning models that is used to provide venue occupancy counts

• Refactored video analytics codebase and made improvements that greatly reduced false positive and error rates