Rudra Bhatt
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with less than a year in Machine Learning & Deep Learning
Highly motivated entry-level AI Engineer with 7 months of experience, specializing in Machine Learning, Deep Learning, and Generative AI. Proven ability to architect automated web scraping pipelines, develop full-stack APIs, and integrate intelligent automation features to enhance efficiency. Skilled in Python, LLM, FastAPI, React, and PostgreSQL, with a strong background in real-time voice ordering platforms, financial news research tools, and AI-driven crop disease detection. Eager to apply technical expertise and problem-solving skills to complex data challenges.
Gujarat Technological University
Diploma · Information Technology
August 1, 2020 – June 30, 2023
Gujarat Technological University
Bachelor of Engineering · Information Technology
N/A – June 30, 2026
Petpooja
Data Science Intern
December 1, 2025 – Present
India
VoiceServe AI – Real-Time Voice Ordering Platform
March 1, 2026 – March 1, 2026
Built real-time AI voice call ordering system integrating Saaras v3 (STT), Kimi K2 (LLM), and Bulbul v3 (TTS) with custom agent architecture supporting dynamic tool execution for menu queries, cart management, validation, and personalized recommendations. Achieved 83% latency reduction (12s→1.5s) by implementing Redis caching with intelligent preloading, stateful session memory, streamlined tool-calling workflows, and optimized STT audio processing with noise filtering and adaptive buffering. Enhanced conversational accuracy through Pinecone vector search with semantic embeddings for fuzzy menu matching, interrupt-aware dialogue management with pause/resume capabilities, and multi-turn turn retention for natural customer interactions. Engineered scalable full-stack AI-powered POS system on FastAPI/React/PostgreSQL stack with FP-Growth-based smart recommendations, menu performance insights, and Gemini-driven multilingual reporting.
View ProjectAI-Powered Financial News Research Tool
October 1, 2025 – October 1, 2025
Engineered an LLM-based application using LangChain, OpenAI API, and Streamlit to retrieve, summarize, and answer queries from real-time financial news data. Optimized retrieval-augmented generation (RAG) pipelines, cutting research time by 60% and showcasing practical deployment of AI models in financial analytics.
AI Driven Crop Disease Detection
December 1, 2024 – December 1, 2024
Contributed to advancing precision agriculture through AI-powered disease detection. Built a deep learning model (CNN, R-CNN) in Python to classify crop diseases with 95.5% accuracy, enabling early detection for farmers. Demonstrated ability to analyze anomalies in large datasets and generate actionable insights with real-world business impact (improved agricultural productivity). Supports farmers by enabling early detection and timely intervention, improving agricultural productivity.
Cultural Fit Analysis
The candidate's projects showcase a strong interest in applying AI to diverse domains, from real-time voice ordering to financial analytics and agriculture, indicating adaptability and a broad perspective. The involvement in hackathons and academic achievements suggests a proactive and competitive spirit. The current internship at Petpooja, involving cross-functional AI collaboration, points to an ability to work within a team and integrate AI solutions into existing ecosystems. The candidate's profile aligns well with an AI Engineer role that requires innovation and practical application of AI technologies.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving and critical thinking skills through project achievements. The internship at Petpooja suggests an ability to work in a team and contribute to internal workflows. The project descriptions are detailed, indicating good communication of technical concepts. However, without direct assessment, the operational fit regarding collaboration and stress handling is inferred from project complexity and role descriptions.