Built FastAPI orchestration services for LLM integration, prompt routing, and structured response validation

Implemented validation layers and fallback mechanisms to handle LLM edge cases in production

Developed backend APIs supporting real-time AI-driven fitness recommendations across mobile platforms

Optimized prompt pipelines and system prompts to improve reliability and consistency of AI-generated programs

Designed modular AI feature architecture enabling scalable integration of additional models and personalization layers

Led end-to-end feature development from concept to deployment for AI-powered mobile experiences

Collaborated with product and engineering teams to iterate on AI features based on user feedback and product roadmap priorities

Built AI-assisted data validation pipelines to detect anomalies in ETL workflows and automatically trigger corrective processing

Developed optimized SQL and data transformation pipelines improving data processing performance by 20%+

Implemented automated error classification and remediation strategies reducing recurring pipeline failures

Engineered log analysis tools to identify root causes of recurring ETL failures

Integrated AI-driven debugging workflows to accelerate issue resolution across data pipelines

Optimized data processing pipelines to reduce latency and improve overall system throughput

Collaborated with engineering teams to integrate AI-assisted debugging and log analysis tools for faster production issue resolution

Designed logging pipeline to capture employee bug data and automate issue triage for engineering workflows

Analyzed UI interaction data and optimized CSS interface components, increasing weekly platform engagement by up to 60%

Implemented automated validation and monitoring tools to track system performance and detect anomalies

Engineered NLP models for automated sentiment analysis and intent detection, reducing critical ticket response times by 40%

Built REST APIs supporting AI-driven ticket processing and classification workflows

Optimized frontend components improving platform engagement by up to 60%

Collaborated with engineering teams to enhance AI-driven product features and workflows

Processed JSON KPI datasets to identify testing variance and improve deployment efficiency by 90%

Optimized MS SQL queries and server-side data pipelines, reducing latency by 40% and improving performance by 20%

Built data validation frameworks to ensure consistency across production systems

Debugged Java mobile systems using Android Studio and log analysis, increasing engineering productivity of up to 50%

Implemented automation tools reducing manual QA effort and improving release cycles

Collaborated with engineering teams to enhance reliability of automotive data systems

Conducted cross-platform testing across Android and iOS devices, achieving 95% deployment readiness

Built location-based search functionality enabling users to find parks based on proximity and features

Designed filtering systems allowing users to discover parks based on amenities and activities

Improved user engagement by 45% through enhanced mobile usability and feature optimization

Led documentation efforts outlining product requirements, system design, and development roadmap

Collaborated with cross-functional teams to align application features with organizational goals

Conducted client meetings to define project scope, timelines, and feature requirements

Worked with a team of 15 engineers to design and deliver a scalable mobile application