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AI Engineer with less than a year in LLM integrated systems and machine learning applications.
Computer Science graduate with hands-on experience building LLM integrated systems, AI automation pipelines, and applied machine learning applications across enterprise and defence domains. Completed internships at TCS and WESEE (Ministry of Defence), delivering AI engines with measurable accuracy and efficiency outcomes. Proficient in Python, prompt engineering, LLM APIs, and explainable AI. Seeking roles in AI application development, AI product analysis, or solutions engineering.
GALGOTIAS UNIVERSITY
Bachelor of Technology · Computer Science and Engineering
August 1, 2022 – June 30, 2026
MOUNT CARMEL SCHOOL UNA
Intermediate (ISC)
N/A – Present
TATA CONSULTANCY SERVICES
AI Application Development Intern
March 1, 2026 – May 31, 2026
India
WESEE, Ministry of Defence, Govt. of India
AI/ML & Application Development Intern
January 1, 2026 – March 31, 2026
India
GUARDIAN AI (CREDIT CARD DECISION INTELLIGENCE SYSTEM)
June 28, 2026 – Present
Developed an explainable AI system combining deterministic MCC rules with an interpretable Random Forest model to recommend credit cards and flag high-risk transactions. Implemented a controlled Retrieval-Augmented Generation (RAG) pipeline with a local LLM (Ollama: Mistral) to generate structured, document-backed recommendations with verifiable explanations.
OSINT CREDIBILITY & VERIFICATION FRAMEWORK (RESEARCH PROJECT)
June 28, 2026 – Present
Designed and implemented an OSINT credibility assessment framework combining source reliability scoring, cross-source verification, and event-level signal analysis using Sentence-BERT (all-MiniLM-L6-v2) for semantic similarity and event clustering. Evaluated across 61 events, achieving 97.99% accuracy, 100% precision, and 98.49% F1 score; incorporated controlled deferral to minimise false positives and improve decision reliability.
AWS Academy Graduate – AWS Academy Cloud Foundations
AWS Academy
June 1, 2026 – Present
AWS Academy Graduate – AWS Academy Cloud Architecting
AWS Academy
June 1, 2026 – Present
AICTE-EduSkills Virtual Internship – AWS Cloud
AICTE-EduSkills
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from credit card decision intelligence to OSINT credibility and underwater acoustic modeling, indicates a broad interest in applying AI across various domains. Their involvement in an Innovation Cell suggests a proactive and collaborative mindset. However, the limited professional experience (internships) means there is less evidence of long-term team collaboration or navigating corporate culture. The target role of 'AI Engineer' aligns well with their demonstrated technical skills and project focus, suggesting a good foundational fit for an AI-centric team.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project work, particularly in optimizing LLM performance and developing complex AI frameworks. Their experience in both enterprise and defense domains suggests adaptability and a structured approach to development. The focus on explainable AI and verifiable recommendations indicates an understanding of responsible AI practices. However, with limited professional experience, their operational fit in a senior role would require further validation of their ability to lead projects, manage stakeholders, and navigate complex organizational structures.