Rakesh Algot

LLM / GenAI Engineer specializing in production-grade RAG systems and retrieval optimization. Experienced in building end-to-end pipelines with hybrid search, reranking, and validation layers, evaluation and monitoring to improve answer accuracy, reduce hallucinations, and optimize inference cost and latency.

AI Backend Engineer at Yensi Solutions (2024-01 – Present)

AI Backend Engineer specializing in building production-grade RAG pipelines and retrieval optimization systems.

• Built end-to-end RAG pipelines with hybrid retrieval, reranking, and response generation for production use
• Improved retrieval accuracy and response quality using cross-encoder reranking and hierarchical chunking
• Implemented local inference (SentenceTransformers + BM25), eliminating external dependencies and reducing cost
• Optimized system performance with Redis caching, improving latency and reducing redundant computation
• Designed scalable async pipelines for high-volume ingestion and background processing
• Developed secure and reliable backend APIs with structured logging and role-based access control

Master of Computer Applications in Computer Applications – Nizam College (2022)