Anis Vahora

AI and full-stack software engineer with 4+ years of experience delivering production applications, data platforms, and AI-enabled operational workflows. Translates business and product requirements into end-to-end solutions spanning React and TypeScript interfaces, Python and Java services, REST APIs, SQL and Redshift data warehouses, AWS QuickSight analytics, and LLM-enabled automation. Delivered workflow automation that reduced manual preparation by 65%, document-processing solutions saving 12–15 hours per week, and data-platform optimizations that improved retrieval latency by 60%.

Software Developer - Chronica - Toronto, Canada

(2022-08)

• Delivered end-to-end capabilities for a production healthcare platform across React, Java, REST APIs, and data workflows, translating operational requirements into features supporting healthcare operations.
• Designed and deployed an AI-driven patient-risk pipeline using AWS Bedrock, MongoDB, Redshift, and ETL to generate structured risk indicators and clinical summaries for application and reporting workflows.
• Built and integrated an LLM-powered workflow automation solution using Java and Amazon Bedrock, reducing manual preparation time by 65%.
• Automated document intake and structured-data extraction from PDFs, EDI 835 files, and images using AWS S3, Textract, and Lambda, eliminating repetitive data entry and saving 12–15 hours weekly.
• Designed and optimized Redshift warehouse models and ETL pipelines supporting AWS QuickSight dashboards and operational reporting, reducing data-retrieval latency by 60%.
• Developed and evaluated a multi-agent RAG pilot using Python, FastAPI, LangChain, AWS Bedrock, and Pinecone to assess retrieval quality, contextual reasoning, and controlled task orchestration.

Full-Stack Developer - Lytikal Insights - San Diego, California

(2022-01 - 2022-07)

• Integrated TradingView charting into the core product to support advanced market visualization and analysis.
• Built portfolio and watchlist modules (Vue.js, Python, MySQL), improving API responsiveness through query and data-fetching optimizations.
• Reduced API retrieval latency by 25% using Python/Flask improvements and query optimizations.
• Containerized services using Docker to streamline deployments and improve development consistency.

Post Graduation - Blockchain Development - George Brown College

Post Graduation - Predictive Analytics - Conestoga College

Bachelor's - Computer Engineering - Gujarat Technological University