Abdelkabir El Bahmadi

– LLM-Based Systems (RAG): Designed and deployed production-grade Retrieval-Augmented Generation (RAG) pipelines in Python, integrating LLMs with vector databases for intelligent knowledge retrieval and response generation. – Multi-Agent Architecture: Architected and implemented multi-agent systems orchestrating LLM reasoning with backend services using LangChain and LlamaIndex to automate recruitment and business workflows. – Interview Evaluation Agent: Built an AI-powered interview agent capable of evaluating candidate responses, generating structured feedback, and automating screening processes end-to-end. – OpenAI & Azure OpenAI Integration: Integrated OpenAI and Azure OpenAI APIs into production pipelines, managing API versioning, rate limits, and cost optimization strategies. – Prompt Engineering & Optimization: Applied advanced prompt engineering strategies, structured system prompts, and evaluation loops to improve reasoning accuracy and response consistency across LLM-powered workflows. – LLM Fine-Tuning Concepts: Worked with fine-tuning methodologies (parameter-efficient approaches, instruction tuning) and model adaptation strategies to improve domain-specific performance. – Embeddings & Semantic Search: Designed semantic search pipelines using embeddings and vector storage to enhance contextual retrieval accuracy at scale. – Knowledge Graph Integration: Integrated structured knowledge representations and graph-based relationships into AI workflows to improve factual consistency and contextual reasoning.

– Full-Stack Development: Designed and developed scalable applications using Next.js (frontend) and Express.js (backend) with secure authentication (JWT, session management). – Microservices Architecture: Restructured backend into 9 microservices to improve maintainability, scalability, and system isolation. – CI/CD & Containerization: Implemented CI/CD pipelines using GitHub Actions and Docker; containerized AI and web services with Docker Compose for dev/test/prod environments. – Production Deployment: Deployed and managed applications on Ubuntu VPS servers, handling infrastructure, monitoring, and high availability for AI-powered services.

– CI/CD Pipelines: Created and managed CI/CD pipelines using Jenkins and GitHub Actions for test and production environments. – Containerization & Infrastructure: Containerized applications with Docker; configured master-slave DB architectures and load balancing for high availability.