Work Experience

Aetna-CVS - Software Development Engineer

June 2024 - July 2025

  • Project: Care Paths AI Platform
  • ML Framework Implementation: Developed personalized health recommendation models with PyTorch and TensorFlow, tailoring care paths for members and improving engagement rates by 25% on GCP.
  • AI-Driven Analytics: Created predictive algorithms in Python to automate claims processing, integrating RAG models for better data retrieval and reducing nurse workload by 90 minutes daily.
  • Cloud Infrastructure Management: Managed AI workloads on Google Cloud Platform, using vector databases for efficient querying and ensuring secure handling of PHI data.
  • Backend Service Enhancement: Built microservices with FastAPI and Java, supporting real-time integrations that streamlined app experiences for chronic condition management.
  • Model Training and Deployment: Trained large language models for member chat interfaces, leveraging Gemini AI and Python scripts to achieve higher accuracy in personalized wellness programs.
  • System Integration: Incorporated asynchronous messaging in C# and Go, connecting AI tools with existing databases like BigQuery, which accelerated development cycles and improved operational automation.

Verizon - Software Engineer

Aug 2023 - May 2024

  • Project: Fiber Cut Prevention AI System
  • AI Model Development: Built machine learning models using Python and PyTorch to analyze over 10 million dig requests, reducing fiber cuts by 15% through predictive analytics on AWS infrastructure.
  • Generative AI Integration: Implemented Llama models on cloud for processing natural language in excavation permits, improving accuracy in risk assessments and integrating with existing ML pipelines.
  • Cloud Deployment: Deployed scalable AI solutions on AWS and Azure, handling high-volume data processing with Kubernetes for containerization and real-time monitoring.
  • Data Pipeline Optimization: Designed ETL processes in Java and Python, incorporating Kafka for streaming data, which enhanced system efficiency and cut processing time by 20%.
  • Cross-Team Collaboration: Worked with data scientists to refine deep learning algorithms in TensorFlow, ensuring compliance with telecom regulations and boosting model performance metrics.
  • Performance Tuning: Optimized ML models for edge computing, using Rust and Go for low-latency components, resulting in faster response times during peak network loads.

OwlSpark - Backend Engineer

May 2022 - Aug 2022

  • Candidate Assessment Module: Delivered a candidate assessment module, integrating voice and video analysis with React.js, Django, Generative AI and openCV, achieving a candidate satisfaction rating of 4.5 out of 5.
  • Golang Microservices: Developed Golang microservices to assess candidate profiles and match them to job openings, leveraging Docker and Kubernetes for deployment, resulting in a 60% improvement in processing speed and accuracy.
  • Messaging Architecture: Engineered lightweight messaging architecture with Django Channels, Web Sockets, Redis with low latency.
  • Monitoring and Analytics: Monitored pod and container statistics by implementing Prometheus and Grafana to collect analytics and identify issues, leading to a 20% cost savings in resource utilization.

WarrantyMe - Software Engineer

Aug 2019 - June 2021

  • UI Development: Designed and developed reusable React.js components using Hooks and Context API, creating a modular UI architecture.
  • Dashbords: Built real-time analytics dashboards with React.js and D3.js, enhancing system performance visibility and user engagement. Implemented secure OAuth 2.0 and JWT-based authentication across the frontend, protecting access to dashboards and APIs for 400+ users.
  • API Integration: Developed RESTful APIs using Flask and PostgreSQL to support real-time data operations for user and admin features. Integrated third-party APIs to extend platform functionality, resulting in improved customer experience and retention.
  • Infrastructure Automation: Deployed applications on AWS EC2 and automated infrastructure provisioning using Terraform, reducing environment setup time by 50%.
  • Monitoring: Monitoring with ELK Stack and AWS CloudWatch, enabling proactive issue detection through real-time log analysis and alerts.