AI Engineer with less than a year in AI/ML & Cybersecurity
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
B.Tech Computer Science student with hands-on experience in AI engineering, cybersecurity, and Python development. Skilled in RAG pipelines, semantic retrieval, vector databases, ML-based security projects, and open-source LLM integration with strong interest in AI-powered security systems.
Vivekananda Global University
B.Tech · Computer Science
August 1, 2025 – Present
Vidya Niketan
Class 12 · CBSE
June 1, 2022 – May 31, 2023
Octopyder Services Pvt. Ltd.
Software & Security Engineering Intern
September 1, 2024 – December 1, 2024
India
Medical AI RAG Assistant
January 1, 2023 – January 1, 2024
Built an AI-powered medical RAG assistant using BGE-M3 embeddings, ChromaDB, and Mistral LLM for diabetes-related querying. Developed complete RAG pipeline including dataset extraction, semantic chunking, embeddings, vector search, and grounded response generation. Built automated PDF/web dataset cleaning and semantic retrieval pipeline with Streamlit chatbot integration.
PhishGuard
January 1, 2023 – January 1, 2024
Developed an ML-based phishing URL detection system using Random Forest classification for real-time phishing identification. Built end-to-end pipeline including dataset preprocessing, feature extraction, evaluation, and prediction workflows.
Client Service Delivery Job Simulation
Third-Bridge (Forage)
February 1, 2026 – Present
Cybersecurity Analyst Job Simulation
Tata (Forage)
June 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects and internship show a strong interest in both AI engineering and cybersecurity, which aligns well with roles that might combine these domains. The diversity of projects (medical RAG assistant, phishing detection) indicates a breadth of interest and adaptability. However, the experience level is entry-level, and the projects are academic, which might require more mentorship in a professional setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on end-to-end solutions, from data preprocessing to deployment. The internship experience suggests an aptitude for problem-solving, debugging, and automation. However, without direct interview data, specific soft skills like teamwork, leadership, or communication in a collaborative setting cannot be fully assessed.