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AI Engineer with 1+ years in Machine Learning & Computer Vision
AI/ML Engineer fresher pursuing B.Tech in Computer Science and Engineering (AI & ML), with hands-on experience building AI applications using Python, FastAPI, RAG pipelines, and computer vision models. Built end-to-end projects involving document intelligence, retrieval systems, real-time inference, and API deployment. Comfortable working on practical AI use cases, collaborating on problem statements, and translating ideas into deployable applications. Interested in applying Machine Learning, Generative AI, and automation to solve real-world engineering challenges.
Dr. Sudhir Chandra Sur Institute of Technology and Sports Complex
B.Tech · Computer Science & Engineering (AI & ML)
August 1, 2023 – Present
WBSCTE
Diploma · Electrical Engineering
N/A – June 30, 2021
Joy Guru Eng. Works (Renuka Sugar Pvt. Ltd.)
Supervisor
April 1, 2023 – August 1, 2023
India
Manpower Group at Adani Solar
Associate
December 1, 2021 – March 1, 2023
India
Real-Time CCTV Analytics – Object Detection & Tracking Pipeline
June 1, 2026 – Present
Built an end-to-end computer vision pipeline for real-time person detection, tracking, and entry-exit counting on CCTV and video streams. Integrated YOLOv8 detection with ByteTrack-based multi-object tracking and line-crossing logic for persistent counting analytics. Optimized inference for lower latency using frame skipping, reduced input resolution, and ONNX-based runtime improvements. Exposed image inference through a FastAPI service and packaged the application with Docker for portable deployment.
View ProjectAI DDR Generator
June 1, 2026 – Present
Built a multi-stage AI pipeline that converts inspection and thermal documents into structured Detailed Diagnostic Reports for engineering-style review workflows. Designed separate extraction, reasoning, scoring, and generation stages so deterministic logic controlled facts while the LLM focused on report writing. Added confidence and severity scoring, conflict detection, and evaluator guardrails to reduce hallucinations and improve report reliability. Implemented offline fallback paths for extraction and report generation so the pipeline can continue even when LLM APIs are unavailable.
View ProjectEnterprise GenAI RAG Assistant
June 1, 2026 – Present
Built an enterprise-ready RAG application that ingests PDF documents, chunks and embeds content, and answers user questions through a FastAPI and Streamlit interface. Implemented FAISS semantic retrieval, BM25 keyword retrieval, and hybrid retrieval routing to improve answer relevance across different query types. Developed API endpoints for upload and Q&A, returning page-level sources and retrieval metadata for traceable responses. Added MLflow-based evaluation and A/B testing to monitor Recall@5, faithfulness, hallucination rate, and latency across retriever variants.
View ProjectGoogle Crash Course on Python
Coursera
June 1, 2026 – Present
Internship (AI & Cyber Security)
SurTech Research & Innovation Center
June 1, 2026 – Present
The Introduction to Data Science Course
Cisco Networking Academy
June 1, 2026 – Present
IBM Granite Models for Software Development - Certification
IBM
March 1, 2026 – Present
n8n – No Code AI Agent Builder | Certification
Unknown
December 1, 2025 – Present
Certificate of Artificial Intelligence Fundamental
IBM
November 1, 2025 – Present
Artificial Intelligence & Machine Learning Training
Internshala Trainings
October 1, 2025 – Present
Cultural Fit Analysis
The candidate shows a strong interest in applying AI to solve real-world engineering challenges, which aligns well with an innovative and problem-solving culture. The diversity of personal projects (Computer Vision, LLM-based document processing, RAG systems) demonstrates a broad interest in AI domains and a self-driven learning attitude. While the previous work experience is not directly technical, it indicates a capacity for structured work and team environments. The pursuit of a B.Tech in Computer Science & Engineering (AI & ML) while also having a diploma in Electrical Engineering suggests a multidisciplinary approach and adaptability.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, attention to detail (e.g., reducing hallucinations, conflict detection), and an understanding of deployment considerations (Docker, FastAPI). Previous non-technical roles suggest experience in team coordination and operational execution, which could translate to a structured approach in technical projects. The focus on real-world engineering challenges indicates a practical mindset.