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AI Engineer with less than a year in Data Science, Deep Learning & Generative AI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data & AI Engineer with hands-on experience in Python, SQL, Excel, and Power BI, skilled in building analytical dashboards, performing EDA, and transforming raw data into actionable business insights. Experienced in Deep Learning (LSTM, RNN) and Generative AI — including RAG pipelines, LLM-powered agents, and real-time AI applications — demonstrated through deployed projects and internship work.
Affordable AI Technologies
Data Science Intern
July 1, 2025 – December 1, 2025
India
RAG-Based Document Q&A System
January 1, 2026 – Present
Built an end-to-end Retrieval-Augmented Generation (RAG) pipeline that allows users to query documents using natural language. Implemented document ingestion, text chunking, and vector embeddings stored in ChromaDB for semantic search. Integrated LangChain retrievers with an LLM to generate context-aware answers grounded in source documents. Deployed as an interactive Streamlit web app with a clean chat interface.
AI-Powered City Assistant with Tool-Calling Agents
January 1, 2026 – Present
Developed a conversational AI agent that autonomously selects and calls external tools to answer user queries. Integrated real-time weather data via OpenWeatherMap API and live news via Tavily search API using LangChain custom tools. Built with Mistral LLM using an agentic loop — the model decides which tool to invoke based on user intent. Deployed on Streamlit with persistent chat session state.
LSTM-Based Next Word Prediction
January 1, 2026 – Present
Trained a multi-layer LSTM neural network on text corpus data for real-time next-word prediction. Preprocessed and tokenized text data into sequential training samples to model language patterns. Deployed an interactive Streamlit app where users type and receive live word suggestions. Tech Stack: Python, TensorFlow/Keras, LSTM/RNN, NumPy, Streamlit.
Cultural Fit Analysis
The candidate's personal projects showcase a strong interest and initiative in cutting-edge AI technologies, aligning well with an innovative and fast-paced AI engineering environment. The diversity of projects (RAG, agentic AI, NLP) indicates a broad curiosity and willingness to explore different AI paradigms. The internship experience in data science suggests an ability to work within a structured team and deliver measurable results. However, the lack of team-based projects outside of the internship means collaboration experience is limited.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and hands-on approach to learning and applying new technologies. The focus on deploying interactive applications suggests an understanding of user experience and practical application of AI. The internship experience shows an ability to work with real-world data and contribute to business outcomes.