AI Engineer with less than a year in Machine Learning and LLM-powered Systems
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AI/ML Engineer focused on building production-oriented machine learning, backend, and LLM-powered systems. Experienced in developing semantic retrieval pipelines, scalable ML workflows, and API-driven applications for structured and unstructured data. Strong foundation in machine learning, problem solving, backend integration, and production-ready system design.
Guru Tegh Bahadur Institute of Technology
B.Tech · Artificial Intelligence & Machine Learning
August 1, 2022 – June 30, 2026
YBI Foundation
Python with ML Intern (Project-Based)
March 1, 2024 – April 1, 2024
India
CodSoft
Python Intern (Project-Based)
December 1, 2023 – January 1, 2024
India
Food Image Classification using Transfer Learning
June 24, 2026 – Present
Built a CNN-based image classification system using transfer learning for improved prediction accuracy Built preprocessing and inference pipeline supporting custom image uploads Improved model performance through fine-tuning and hyperparameter optimization
AI PDF Chatbot – Retrieval-Augmented Generation System
June 24, 2026 – Present
Built an end-to-end Retrieval-Augmented Generation (RAG) system for contextual querying over large documents Built semantic retrieval workflow using embeddings and FAISS for efficient context retrieval Integrated LLM pipelines with semantic retrieval to generate context-aware responses Designed modular ingestion and retrieval pipeline supporting scalable document processing
View ProjectKepler Signal Review System
June 24, 2026 – Present
Developed a machine learning pipeline for classification and prioritization of astronomical signal data Achieved 89% recall and reduced manual analysis workload by 70% Performed feature engineering, preprocessing, and model optimization for improved reliability
View ProjectCultural Fit Analysis
The candidate's projects demonstrate a strong interest and practical application in AI/ML, aligning well with an AI Engineer role. The diversity of projects (image classification, NLP/RAG, astronomical data processing) indicates adaptability and a broad interest in different problem domains. The focus on building 'production-oriented' systems suggests a practical, results-driven mindset. However, the experience is primarily academic and project-based, with limited industry exposure, which might require some adjustment to a corporate culture.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-component systems (e.g., RAG chatbot, Kepler Signal Review System). The focus on 'production-oriented machine learning' and 'scalable ML workflows' suggests an understanding of operational considerations. However, without direct interview data or psychometric test results, it's difficult to fully assess soft skills like teamwork, communication in a collaborative setting, or stress handling.