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AI Engineer with less than a year in Data Analytics & Generative AI
Recent MSc graduate in Data Analytics and Computational Science with hands-on experience building Generative AI applications, LLM-integrated pipelines, RAG workflows, and agentic AI systems. Proficient in Python, LangChain, FastAPI, Docker, and PyTorch, with practical experience in vector search using FAISS, embeddings-based retrieval, and prompt engineering. Experienced in designing and deploying AI-powered applications and REST APIs, with familiarity with cloud platforms (GCP) and MLOps practices. Strong foundation in machine learning, deep learning, and NLP, with a track record of building and shipping real-world Generative AI solutions. Published researcher with strong problem-solving, communication, and collaboration skills.
Kerala University of Digital Science, Innovation and Technology (DUK)
MSc · Data Analytics and Computational Science
August 1, 2024 – June 30, 2026
St. Aloysius College
BSc · Mathematics and Statistics
August 1, 2021 – June 30, 2024
GNX Digital Solutions
Data Analyst Intern
January 1, 2026 – May 1, 2026
Thiruvananthapuram, Kerala, India
Agentic AI Mobile Recommendation System
June 1, 2026 – Present
• Designed and built a multi-agent AI system using LangChain with autonomous agents for filtering, ranking, and evaluating products from conversational natural language input. • Implemented FAISS-based vector database search and embeddings-driven RAG-style retrieval workflows for semantic product matching and real-time recommendations. • Integrated third-party data sources via web scraping pipelines and REST APIs, transforming raw inputs into structured model-ready datasets. • Generated explanation-based recommendations using LLM-driven evaluation, demonstrating end-to-end Generative AI and Conversational AI chatbot workflow design. • Architected the system as composable, independently testable agent modules - suited for integration into larger enterprise AI platforms and chatbot applications.
AI-Powered Business Analytics Copilot
June 1, 2026 – Present
• Built a Generative AI application integrating LangChain and LLMs to convert natural language queries into SQL, enabling automated business data retrieval and AI-powered analytics. • Designed RAG-style retrieval workflows combining LLM reasoning with structured data sources via BigQuery and GCP for accurate, context-aware responses. • Engineered prompts and few-shot examples to optimize LLM accuracy, reduce hallucinations, and improve query generation reliability. • Developed and deployed FastAPI backend to serve AI capabilities as REST APIs; containerized the application using Docker for consistent deployment. • Monitored AI application performance and output quality; documented model behavior, data flows, and limitations to support governance and auditability.
Career Compass – NLP-Based Career Path Recommender
June 1, 2026 – Present
• Built an NLP-powered AI application using TF-IDF vectorization and FAISS vector database search - a lightweight RAG pipeline for profile-to-career path matching. • Designed end-to-end NLP pipelines for text ingestion, preprocessing, feature extraction, and semantic similarity analysis using spaCy and embeddings-based cosine similarity. • Designed modular, API-ready pipeline components suitable for integration into a larger LLM-powered chatbot or conversational AI platform.
Smart Fraud Detection & Monitoring System
June 1, 2026 – Present
• Built a complete ML pipeline: data collection, preprocessing, feature engineering, model training, evaluation, and deployment via FastAPI; containerized using Docker. • Developed PostgreSQL database schemas and REST APIs for real-time model inference and result storage. • Monitored model performance and generated analytics dashboards using Power BI to evaluate AI application behavior and support business decisions.
Autonomous PPE Compliance Monitor for Industrial Safety
June 1, 2026 – Present
• Built a real-time AI application using YOLOv8 and DeepSORT for live video stream analysis - demonstrating real-time AI integration and deployment capability. • Developed end-to-end deep learning pipeline: preprocessing, training, evaluation, and GPU-optimized inference; deployed via FastAPI and Docker. • Monitored model performance across detection classes and optimized pipeline configuration for real-time accuracy and efficiency.
Google Cloud Career Launchpad - Data Analytics Track
Google Cloud
June 1, 2026 – Present
Google Cloud Foundations - Fundamentals, Infrastructure, Networking & Data-ML-AI
Google Cloud
June 1, 2026 – Present
Prepare Data for ML APIs - Google Cloud
Google Cloud
June 1, 2026 – Present
Introduction to Git and GitHub
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, covering business analytics, industrial safety, recommendation systems, and fraud detection, indicates a broad interest in applying AI across different domains. The academic projects and internship align well with an AI Engineer role, showcasing a proactive approach to learning and applying cutting-edge AI technologies. However, the lack of extensive professional experience beyond an internship might require additional mentorship to fully integrate into a fast-paced industry environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving, communication, and collaboration skills through project descriptions and a published research paper. Their experience in Agile workflows and documenting system architecture indicates good operational fit for team-based development environments. The academic nature of most projects suggests a strong theoretical foundation, but real-world operational experience is limited to an internship.