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AI Engineer with less than a year in Generative AI & Agentic Systems
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Final-year B.Tech student in Artificial Intelligence & Data Science with hands-on experience building LangGraph-based agentic systems, RAG pipelines, and WhatsApp-native AI applications. Skilled in LangChain, FAISS, FastAPI, and multi-agent orchestration. Built a production-ready Restaurant AI Agent targeting the Indian market featuring intent routing, multi-turn order management, and real-time NLP. Passionate about translating cutting-edge generative AI research into practical, deployable products.
Sagi Rama Krishnan Raju Engineering College
B.Tech · Artificial Intelligence & Data Science
August 1, 2022 – June 30, 2026
Sri Chaitanya College
Board of Intermediate Education
N/A – May 31, 2022
Restaurant AI Agent - WhatsApp-Native Multi-Agent System
January 1, 2025 – January 1, 2025
Designed and built a production-grade agentic AI system using LangGraph with 7 specialist nodes: IntentRouter, OrderAgent, ReservationAgent, FAQAgent (FAISS RAG), FeedbackAgent, UpsellAgent, and EscalationAgent. Integrated Twilio WhatsApp API via FastAPI webhooks to deliver a fully WhatsApp-native ordering and reservation experience for Indian restaurants. Implemented multilingual intent classification using Groq LLM (Llama 3), supporting Hindi, Telugu, and English targeting India's linguistically diverse restaurant customer base. Built a FAISS-backed RAG knowledge base for instant FAQ resolution, reducing escalation rate and improving response accuracy. Developed a Next.js owner dashboard with a real-time chat simulator, order analytics, and menu management portal. Architected multi-tenant support with isolated FAISS indices per restaurant_id, enabling SaaS-style deployments.
AI-Driven PDF Processing & Multilingual Translation (RAG Application)
January 1, 2024 – January 1, 2024
Built an end-to-end RAG pipeline combining document retrieval and generative AI to process and summarize complex academic PDFs including research papers, textbooks, and notes. Enabled multilingual translation across English, Hindi, Telugu, and Spanish using the Google Translate API integrated within the LangChain pipeline. Generated AI-powered mind maps for conceptual visualization, improving study efficiency and knowledge retention.
AgroSakha AI-Powered Farming Assistant
January 1, 2024 – January 1, 2024
Led the AI/ML domain in a multidisciplinary team; designed and trained models for crop recommendation, fertilizer prediction, and plant disease detection using TensorFlow and PyTorch. Integrated real-time weather, soil sensor, and government scheme data via REST APIs to generate personalized, actionable farming insights. Deployed the system as a Flask web application with a farmer-friendly interface, enabling non-technical users to interact with ML predictions.
Participant - 10-Day Cyber Security & Ethical Hacking Workshop
Unknown
January 1, 2024 – Present
Runner-up - Football Tournament, Inter-Department Event
Unknown
January 1, 2024 – Present
Winner - Code Master Contest, College-level Technical Coding Competition
Unknown
January 1, 2024 – Present
Winner - Smart India Hackathon (SIH) Internal Round, SRKR Engineering College
SRKR Engineering College
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's projects show a strong alignment with the AI Engineer role, particularly in generative AI, agentic systems, and RAG. The diversity of projects (WhatsApp agent, PDF processing, farming assistant) indicates a broad interest in applying AI to different domains. Participation in hackathons and coding contests suggests a competitive and continuous learning mindset. However, the lack of professional experience and focus on academic/personal projects means cultural fit in a corporate environment, especially regarding long-term team collaboration and navigating organizational structures, is yet to be fully demonstrated.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and leadership through leading the AI/ML domain in a team project and winning technical competitions. The detailed project descriptions suggest good communication of technical concepts. The focus on deployable products and real-world problem-solving indicates a practical, results-oriented approach. However, without specific psychometric or English test scores, a comprehensive assessment of work attitude, stress handling, and team collaboration is limited.