Abdallah Hwishel

Building agentic AI solutions end to end: LLM-powered APIs and SDKs, prompt-engineering services, and real-time chat and WhatsApp integrations.

• Developed and deployed end-to-end agentic solutions to automate customer touchpoints, optimizing resource allocation and scaling the conversion-led support architecture.
• Worked with the CEO to design and deliver accessible AI solutions for car dealerships, improving accuracy and speed for inspectors and buyers.
• Built and scaled APIs and SDKs for integrating AI models and agentic systems that automate customer touchpoints across our BuyAnyCar social platform.
• Implemented a modular prompt building service that increased prompt production rate for our AI agents by 50%.
• Designed and maintained scalable backend services for a car scanning system that integrates large language model capabilities to automate detail identification and improve operational speed by 40%.
• Architected and scaled APIs to support real-time chat functionality and WhatsApp integration across multiple applications to ensure seamless communication between buyers and sellers.
• Enhanced legacy CRM systems and optimized database performance by 30% to ensure high availability and improved efficiency for inspectors and dealers.
• Implemented social networking features within the BuyAnyCar application, enhancing user engagement and retention.
• Strengthened platform security and operational health by resolving critical vulnerabilities and implemented API rate limiting to prevent brute-force attacks.
• Authored comprehensive technical documentation and system architecture diagrams, reducing onboarding time for new hires.
• Collaborated with product managers and designers to translate ambiguous requirements into technical specifications for high-impact initiatives.

Backend engineer on a software lifecycle analysis tool, engineering AI-driven features on AWS Bedrock and LangChain.

• Designed and maintained scalable backend services for a software lifecycle analysis tool that integrated real-time data analytics to increase team efficiency by 25%.
• Identified and resolved a critical bottleneck in our cataloging module by implementing message queues using AWS SQS, improving system throughput by 40%.
• Engineered AI-driven applications that leverage large language models, AWS Bedrock and LangChain, streamlining data processing tasks and enabling personalized user experiences, which improved marketing campaign targeting and customer engagement by 30%.
• Implemented rigorous maintenance routines and industry best practices for quality assurance to ensure high availability and reduce system downtime.
• Mentored new team members on best practices for Python and FastAPI, resulting in quicker onboarding and increased delivery velocity.
• Optimized database schemas and queries to ensure low latency and a 20% increase in data retrieval speed for client-facing applications.
• Strengthened the backbone of application governance by integrating authentication protocols and security best practices to ensure an auditable and secure production environment.

Full-stack engineer on a crop planning and weather forecasting platform for the Earth System Science Interdisciplinary Center.

• Built a dynamic management dashboard for crop planning and weather forecasting using React and Material UI.
• Developed interactive data visualizations that improved data processing efficiency for users by 30% and greatly improved resource allocation.
• Designed responsive widgets to display real-time meteorological data across mobile and desktop devices.
• Automated real-time data updates via websockets, ensuring users have access to the most current forecasts.
• Integrated multiple third-party APIs to fetch and sync live weather and agricultural data.