Sami Melhem Portfolio

Core Concepts I'm Proficient In:

• Production LLM SDK Integration: Comprehensive experience implementing LLM SDKs in real-world applications across internships, jobs, and research projects, with deep understanding of documentation analysis and practical implementation strategies

• Claude SDK Optimization: Expert use of Anthropic's Claude SDK for reliable startup evaluation report generation at PitchFact, ensuring consistent and accurate business intelligence processing

• Multi-Platform API Implementation: Strategic integration of multiple LLM APIs including OpenAI, Hume.ai, and Anthropic across different project contexts and requirements

• Documentation-Driven Development: Advanced approach to LLM API integration through comprehensive documentation analysis and systematic implementation for specific problem-solving contexts

• Educational LLM Applications: Sophisticated use of ChatGPT Agent store and multiple models to create educational tools and research applications

• Research-Grade LLM Integration: Implementation of LLM APIs for academic research applications including autonomous vehicle communication and medical training simulations

• Multi-Model Architecture: Practical experience with inter-model communication and coordination in complex research environments requiring sophisticated AI agent interactions

Advanced Development Patterns:

• Resource Optimization Strategy: Systematic approach to understanding and optimizing LLM resource utilization through comprehensive documentation study and practical experimentation

• Reliability-Focused Implementation: Strategic selection of LLM APIs based on reliability requirements, particularly Claude SDK for mission-critical startup evaluation processes

• Problem-Specific API Selection: Advanced decision-making process for choosing appropriate LLM APIs based on specific application requirements and performance characteristics

• Research Integration Methodology: Systematic approach to implementing cutting-edge LLM research including autonomous vehicle agentic AI communication and court simulation applications

• Cross-Application LLM Architecture: Development of LLM integrations that span multiple application contexts from business intelligence to educational tools and research simulations

• Documentation-First Integration: Strategic methodology that prioritizes comprehensive understanding of API capabilities before implementation to ensure optimal resource utilization

Complex Problem-Solving Examples:

Claude SDK Startup Evaluation Pipeline at PitchFact: Implemented a sophisticated Claude SDK integration for generating comprehensive startup evaluation reports that require high reliability and consistency for business decision-making. The challenge involved ensuring the LLM API could process diverse startup data and generate actionable intelligence reports that meet professional standards for angel investment evaluation. Successfully developed a Claude-based pipeline that consistently delivers accurate startup analysis, financial projections, and investment recommendations, enabling PitchFact to streamline the startup evaluation process while maintaining quality and reliability standards.

Multi-Model Court Simulation System for Medical Training: Architected a complex multi-model LLM system for court simulation applications in medical training contexts, requiring coordination between different AI agents with specialized roles and inter-model communication protocols. The challenge involved creating a system where multiple LLM APIs could communicate effectively while maintaining their individual specializations and ensuring realistic simulation scenarios. Successfully implemented a multi-agent architecture that uses different LLM models for various simulation roles, creating an immersive training environment for medical professionals with realistic courtroom dynamics and educational value.

Autonomous Vehicle Agentic AI Research at Algoverse: Conducted cutting-edge research into autonomous vehicle communication using agentic AI systems, implementing the latest LLM API research to explore vehicle-to-vehicle and vehicle-to-infrastructure communication protocols. The project required staying current with rapidly evolving LLM capabilities and implementing experimental features for research purposes. Successfully integrated advanced LLM APIs to model complex communication scenarios between autonomous vehicles, contributing to research understanding of how AI agents can coordinate in real-world transportation systems.

Educational Tool Development Using ChatGPT Agent Store: Developed sophisticated educational applications leveraging ChatGPT Agent store capabilities and multiple LLM models to create comprehensive learning tools for students. The challenge involved integrating multiple model capabilities while maintaining educational effectiveness and user engagement. Successfully created educational platforms that utilize different LLM strengths for various educational functions, from content explanation to assessment and personalized learning path generation.

Areas for Continued Growth:

• Advanced LLM Resource Optimization: Mastering techniques to maximize LLM utilization efficiency, cost optimization, and performance tuning across different API providers for enterprise-scale applications

• Authentication & Security Architecture: Implementing comprehensive security frameworks including data protection, secure API key management, and privacy-preserving LLM integration for sensitive applications

• Custom LLM Fine-Tuning: Developing expertise in fine-tuning LLM APIs for specialized applications, custom model training, and domain-specific optimization to create highly specialized AI solutions

• Embeddings & Vector Integration: Mastering LLM embedding APIs, vector databases, and semantic search integration for advanced retrieval-augmented generation and knowledge management systems

• Custom Feature Development: Learning to extend LLM capabilities through custom API development, function calling, and specialized feature integration for unique application requirements

• Enterprise LLM Architecture: Developing skills in large-scale LLM deployment, multi-tenant systems, and enterprise-grade LLM API management for production-scale applications

LLM APIs

My Experience

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Projects Using LLM APIs

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