AI Engineer with less than a year in Computer Vision, LLMs & RAG for scalable solutions.
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AI/ML Developer specializing in computer vision pipelines and agentic workflows. Experienced in building end to end machine learning solutions, from training custom CNN and object detection models to developing real time inference APIs with FastAPI. Proficient in structuring agentic workflows and RAG systems using LangGraph. Active Kaggle practitioner and open source builder focused on scalable, functional code.
Karimganj College
BCA · Computer Applications
August 1, 2023 – June 30, 2026
Karimganj College
Class XI - XII
N/A – Present
Jawahar Navodaya Vidyalaya (JNV)
Class X · Secondary
N/A – Present
TrafiSight (Intelligent Traffic Surveillance Pipeline)
June 1, 2023 – June 1, 2026
Engineered a scalable computer vision pipeline for real-time traffic surveillance with vehicle detection. Integrated OCR for license plate reading and multi-object tracking for vehicle trajectory analysis. Automated traffic violation detection with structured logging to SQLite; modular agent-based architecture for extensibility.
View ProjectDynamic Fashion Pricing Engine
June 1, 2023 – June 1, 2026
Built a dynamic pricing prediction API for fashion items using regression models including XGBoost and Random Forest. Performed EDA, feature engineering, and scikit-learn preprocessing pipelines to prepare structured retail data. Exposed predictions via a REST API for downstream integration.
View ProjectLazyBot (Daily Accountability Companion)
June 1, 2023 – June 1, 2026
Designed a low noise AI companion that sends one meaningful daily message to help users stay focused on personal goals. Demonstrates applied prompt engineering and lightweight agent design for behavior-change use cases.
View ProjectSkintelligent (Dermatology Screening Assistant)
June 1, 2023 – June 1, 2026
Built a smart dermatology assistant that identifies affected skin regions and classifies conditions using deep learning. Designed for accessible, early skin health screening with visual explainability outputs. Served as the foundational research prototype iterated into the PIPI production pipeline.
View ProjectPIPI (Patient Intelligence & Processing Interface)
June 1, 2023 – June 1, 2026
Built a unified AI-powered medical care-navigation platform integrating skin image analysis, medical document extraction, Voice assisted scheduling, and automated clinician handoff. Developed SKIN_TELLIGENT v2.0 computer vision pipeline for 27-class skin condition classification, with region-based detection and Grad-CAM++ explainability. Improved model from v1.0 to v2.0: Accuracy 71.1% → 82.3%, Macro Recall 77.0% → 91.4%, Macro F1 70.1% → 84.1%. Built voice agent (ElevenLabs) with LangGraph-powered tool orchestration for patient registration, doctor search, scheduling, and in-call image upload triggering. Automated pre-visit doctor handoff: generates a PDF case packet (fpdf2) and emails it with original uploads via SMTP on appointment booking. Containerized full stack with Docker Compose; CI/CD pipeline via GitHub Actions.
View ProjectCultural Fit Analysis
The candidate's portfolio showcases a diverse range of AI applications, from healthcare to traffic surveillance and fashion pricing, indicating adaptability and broad interest within the AI domain. The focus on building practical, impactful solutions aligns well with an innovative and results-oriented culture. The self-learning and open-source contributions suggest a proactive and growth-oriented mindset. However, the lack of team-based projects or professional experience makes it challenging to fully assess cultural fit in a collaborative work environment.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and self-learning capabilities through extensive personal projects and Kaggle participation. The project descriptions indicate an ability to work on complex, multi-faceted problems and iterate on solutions. The focus on end-to-end solutions and CI/CD suggests an understanding of operational aspects. However, without direct work experience, it's difficult to assess team collaboration, stress handling, or direct communication in a professional setting.