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AI Engineer with less than a year in Generative AI & ML Workflows
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Assessing your cultural and operational fit
Highly motivated and results-oriented Integrated M.Tech student in Artificial Intelligence with a strong foundation in Python, Java, and SQL. Possessing expertise in AI/ML, FullStack development, and Cloud & DevOps, I have developed end-to-end ML workflows on GCP, built scalable data processing pipelines, and implemented context-aware factuality checks in LLM evaluation frameworks. My experience includes developing AI-powered platforms for mental health and voice/chat assistance, demonstrating a passion for leveraging AI to solve real-world problems and improve efficiency.
Vellore Institute of Technology
Integrated M.Tech · Artificial Intelligence
August 1, 2022 – June 30, 2027
Google Cloud (SmartInternz SmartBridge)
Generative AI Intern
January 1, 2026 – Present
India
LLM-Sentinel (LLM Evaluation Pipeline)
January 1, 2026 – February 1, 2026
Built a semantic scoring engine using sentence-transformers and FAISS for fast, embedding-based similarity evaluation. Implemented context-aware factuality checks by validating model responses against ground-truth embeddings. Designed an LLM evaluation framework to assess hallucination, relevance, and factuality without relying on costly LLM-based judges.
NeuroQ (AI Powered Mental Health Platform)
January 1, 2025 – March 1, 2025
Built scalable backend services using FastAPI to support real-time emotional tracking and ML-driven analytics. Developed an NLP-based journaling assistant using text classification to detect anxiety signals, achieving 65%+ precision. Designed a data-driven recommendation system to suggest activities based on user behavior and emotional patterns, improving engagement by 30%.
EchoAI (Voice & Chat Assistant)
August 1, 2024 – October 1, 2024
Engineered an end-to-end AI pipeline integrating LLM, OCR, and speech processing for automated data extraction and analysis. Improved text extraction efficiency by 75% and reduced manual effort by 80% through optimized preprocessing. Structured modular ML workflows enabling reuse across multiple use cases and scalable system deployment.
AWS Certified Machine Learning Engineer - Associate - MLA-C01
AWS
April 1, 2026 – Present
A Comparative Benchmarking Study of Classical Machine Learning and Deep Learning Methods for Image-Based Deepfake Detection (Published)
Unknown
April 1, 2026 – Present
Oracle Certified Generative AI Professional
Oracle
October 1, 2025 – Present
Oracle Certified Data Science Professional
Oracle
October 1, 2025 – Present
HackerRank Certified Advanced SQL
HackerRank
March 1, 2025 – Present
Applied Machine Learning in Python
Unknown
December 1, 2023 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio (LLM evaluation, mental health platform, voice/chat assistant) and participation in hackathons suggest a proactive, innovative, and curious mindset. The certifications and ongoing education indicate a commitment to continuous learning, which aligns well with a dynamic tech culture. The current internship at Google Cloud further enhances their fit for a professional, high-performance environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems, suggesting strong problem-solving skills. The focus on modular design and scalable services points to an understanding of operational best practices. However, without direct interview data, assessing collaboration, adaptability, and communication in a team setting is difficult.