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AI Engineer with less than a year in LLM-powered applications & RAG pipelines
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AI and Data Science Engineer with hands-on experience in LLM-powered applications, Retrieval-Augmented Generation (RAG), Multi-Agent Systems Agentic AI · LLM Orchestration, and end-to-end ML pipelines. Proficient in Python, LangChain, LangGraph, FAISS, and Generative AI. Focused on building scalable, intelligent solutions for real-world use cases.
Trident Academy of Creative Technology
Master of Computer Applications (MCA)
January 1, 2024 – Present
Sophitorium Institute of Technology and Life Skills
Bachelor of Computer Applications (BCA)
January 1, 2021 – January 1, 2024
Info Bharat Interns
Data Science Intern
January 1, 2026 – Present
India
Multimodal RAG – E-Commerce Product Assistant
January 1, 2026 – Present
Built a Multimodal RAG system for text- and image-based product search using CLIP embeddings. Implemented FAISS semantic vector search, achieving 40% improvement in retrieval accuracy over keyword search. Evaluated retrieval quality using Precision, Recall, and F1-score to benchmark model performance. Deployed a FastAPI REST API pipeline for real-time user interaction with the multimodal AI system.
Report Summarization Multi-AI Agent System
January 1, 2026 – Present
Built a multi-agent AI system using CrewAI where specialized agents collaboratively summarize long reports. Designed agent roles (Researcher, Analyst, Writer) powered by Gemini AI (LLM) for structured task delegation. Processed 100+ documents end-to-end, reducing manual summarization time by 60%. Evaluated output quality using Accuracy and F1-score metrics to validate summarization performance.
Introduction to Industry 4.0 and Industrial Internet of Things (IIoT)
IIT Madras (NPTEL)
June 1, 2026 – Present
AI Agents with CrewAI
CampusX
December 1, 2025 – Present
Prompt Engineering for ChatGPT
DeepLearning.AI / Coursera
August 1, 2025 – Present
Cultural Fit Analysis
The candidate shows a strong interest in cutting-edge AI technologies (Multi-AI Agents, Multimodal RAG, Prompt Engineering) and continuous learning through certifications. The projects are diverse, covering summarization and e-commerce search, indicating adaptability. The focus on building scalable, intelligent solutions aligns well with an innovative and growth-oriented culture. However, the experience is primarily academic and internship-based, which might require mentorship in a fast-paced industry setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-component systems and a focus on quantifiable improvements (e.g., reducing manual effort by 30%+, improving accuracy by 35%). This suggests a results-oriented approach and problem-solving skills. The multi-agent system project also implies an understanding of task delegation and system orchestration, which are valuable for operational fit in complex AI projects.