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Generative AI Engineer with less than a year in LangChain, RAG, and AI Agent development.
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Shubham Mohanty is an aspiring Generative AI Engineer with a solid foundation in Computer Science and hands-on experience in cutting-edge AI technologies. Through an intensive internship and various projects, he has honed skills in building LLM-powered applications, deploying intelligent AI Agents, and developing end-to-end RAG systems. His expertise spans across LangChain, LangGraph, vector databases, and prompt engineering, enabling him to craft innovative solutions for real-world problems.
Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh
B.Tech · Computer Science & Engineering
August 1, 2020 – June 30, 2024
Innomatics Research Labs
Generative AI Intern
November 1, 2025 – February 1, 2026
India
YOGA RECOMMENDATION SYSTEM USING RAG
June 1, 2026 – Present
Designed and implemented a Retrieval-Augmented Generation (RAG) system that recommends yoga practices for specific diseases using a structured knowledge base from Encyclopaedia of Yoga for Common Diseases, providing step-by-step yoga guidance along with causes and contraindications. Performed document preprocessing including text chunking, embedding generation, and vector storage to enable efficient semantic retrieval of relevant medical and yoga information. Developed an end-to-end pipeline including document loading, text chunking, embedding generation, vector storage, and response synthesis. Integrated a Large Language Model (LLM) with the retrieval pipeline to generate refined, user-friendly responses to health queries, improving answer relevance and clarity.
AI Mentor Chatbot
June 1, 2026 – Present
Built an AI-powered mentor chatbot delivering strictly domain-specific guidance using Streamlit, LangChain, and Hugging Face LLMs. Implemented system-level prompt enforcement to restrict responses exclusively to selected modules (Python, SQL, EDA, ML, DL, GenAI, Agentic AI). Designed a mentor persona with configurable industry experience, enabling interview-focused and learner-level-aware explanations. Integrated session-based conversational memory and dynamic prompt routing for coherent, context-aware mentoring. Developed a modern ChatGPT-style UI with chat history export for revision, documentation, and portfolio usage.
REAL-TIME SIGN LANGUAGE RECOGNITION SYSTEM
June 1, 2026 – Present
Built a real-time system that translates hand gestures into text and speech to bridge communication gaps for the Deaf and Hard of Hearing community. Implemented hand landmark detection using MediaPipe and classified gestures with a Random Forest model, achieving accurate recognition of letters, words, and commands. Integrated text-to-speech (pyttsx3) for audio output and sentence construction, enabling seamless two-way communication. Utilized Python, OpenCV, MediaPipe, scikit-learn, and pyttsx3, ensuring efficient real-time performance. Designed the system to recognize commands like Start, Stop, Space, Backspace for dynamic sentence building and user-friendly interaction.
Python Fundamentals
Unknown
June 1, 2026 – Present
SQL For Data Engineering
Unknown
June 1, 2026 – Present
Data Science
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects show a strong interest in applying AI to solve real-world problems, from healthcare recommendations to accessibility for the deaf community. This indicates a proactive and socially conscious approach, which could be a good cultural fit for organizations valuing impact-driven innovation. The breadth of skills across ML, DL, GenAI, and data engineering suggests adaptability and a continuous learning mindset. The personal projects demonstrate initiative and self-direction, which are valuable traits in a dynamic team environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through diverse project implementations. The detailed descriptions of project architecture and technical choices suggest an organized and methodical approach to development. The focus on user-friendly interfaces and real-world applications (e.g., sign language recognition, yoga recommendation) indicates a user-centric mindset. The internship experience highlights an ability to work with advanced tools and contribute to complex AI pipelines.