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AI Engineer with 2+ years in RAG Systems & LLM Applications
AI Engineer with about 2 years of production experience in building large-scale Retrieval-Augmented Generation (RAG) systems and LLM-powered applications. Specialized in developing end-to-end AI pipelines from data acquisition to deployment, with expertise in processing 1M+ documents and achieving measurable efficiency gains. Passionate about leveraging cutting-edge AI technologies to solve real-world problems at scale. Proven track record in web scraping, semantic search, embeddings generation, and multimodal AI systems. Seeking opportunities to contribute to innovative AI projects in fast-paced, technology-driven environments.
THDC Institute of Hydropower Engineering and Technology
Bachelor of Technology · Computer Science and Engineering
August 1, 2019 – June 30, 2023
RediMinds, Inc
AI Enablement Engineer
October 1, 2024 – Present
India
RediMinds, Inc
AI Enabler Intern
May 1, 2024 – September 1, 2024
India
Production RAG System for Research Intelligence
June 29, 2026 – Present
Architected and deployed an end-to-end RAG pipeline processing 1M+ research documents from 40+ sources for policy research teams. Implemented multimodal performance evaluation framework integrating text and image modalities to compute answer confidence scores and quantify retrieval-response alignment. Developed evaluation-free knowledge-grounded answering mechanism ensuring outputs were directly traceable to source documents with reference links for verification and auditability.
View ProjectSalish Sea Digital Twin - Oceanographic Simulation System
June 29, 2026 – Present
Built a physics-based digital twin to simulate fish movement and calculate energy potential using custom data structures integrated with Unreal Engine for real-time simulations. Integrated OpenDrift particle drift simulations with Unreal Engine to visualize interactions across surface, underwater, and seafloor environments. Achieved 94% accuracy in fish classification and 97% accuracy in ocean current prediction through optimized ML model deployment.
Multilingual Semantic Search & Translation Module
June 29, 2026 – Present
Implemented multilingual translation module leveraging Hugging Face transformers to enable cross-language data retrieval and expand system usability across linguistic domains. Generated and managed text embeddings in PostgreSQL providing semantic similarity search without reliance on external vector databases. Integrated translation capabilities with LLM-based retrieval pipelines to significantly reduce hallucinations and improve answer relevance across languages.
Cultural Fit Analysis
The candidate's project diversity, ranging from RAG systems for research intelligence to oceanographic digital twins, indicates adaptability and a broad interest in applying AI to different domains. Their experience in a remote setting and collaboration with cross-functional teams suggests a good fit for dynamic, technology-driven environments. The focus on production-grade systems and measurable efficiency gains aligns with a results-oriented culture. The candidate's interests in chess and fitness suggest a well-rounded individual.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their work on resilient web-scraping systems and hallucination mitigation. Their experience in partnering with cross-functional teams indicates good collaboration and communication. The ability to independently integrate ML models and optimize simulations suggests a proactive and results-oriented approach. The candidate's projects show a capacity for end-to-end system development and a focus on real-world applicability.