Hi, I'm Sanikommu Venkata Ganesh Reddy

AI Engineer

Passionate about building intelligent systems that solve real-world problems through Artificial Intelligence.

Experience View Projects
Sanikommu Venkata Ganesh Reddy

About Me

Career Objective

Recent Computer Science graduate specializing in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and Agentic AI systems. Proficient in Python, LangChain and LangGraph, with hands-on project and internship experience building RAG-based intelligent assistants with vector databases such as FAISS and Pinecone. Eager to contribute this hands-on foundation to a team building scalable, production-grade Generative AI and Agentic AI systems.

9.17

CGPA

8+

Certifications

4+

Major Projects

1

Hackathon Participation

Location

Rentachintala, Palnadu District, Andhra Pradesh

Education

B.Tech at SRKR Engineering College

Specialization

Artificial Intelligence and Machine Learning

Education

Bachelor of Technology

SRKR Engineering College, Bhimavaram

2022 - 2026

CGPA: 9.17

Completed B.Tech in Computer Science and Engineering with a strong academic record and a focused specialization in Artificial Intelligence and Machine Learning.

Honors Degree

SRKR Engineering College, Bhimavaram

2023 - 2026

CGPA: 8.30

Earned an Honors Degree in Computer Science and Engineering, demonstrating advanced proficiency and academic excellence in AI/ML technologies.

Intermediate (12th Grade)

Vignan CO-Operative Junior College, Vadlamudi

2020 - 2022

Percentage: 97.6%

Completed intermediate education with outstanding performance in Mathematics, Physics, and Chemistry, building a strong foundation for engineering studies.

Secondary School Certificate (10th Grade)

Narayana English Medium School, Guntur

2019 - 2020

Percentage: 94.3%

Completed secondary education with excellent academic performance across all subjects.

Experience

BLACKBUCK ENGINEERS PVT. LTD.

Artificial Intelligence and Machine Learning Engineer Intern

Dec 2025 – April 2026 (4 months)
Key Achievements & Learning:
    • Engineered a real-time Student Attention Monitoring and Analysis System utilizing Computer Vision and Deep Learning (CNN/MobileNetV2), achieving 91% accuracy in engagement detection.
    • Deployed and continuously monitored the AI system on Google Cloud, successfully transitioning the model from a local environment to a live, scalable cloud architecture.
    • Integrated OpenCV for live video feed processing to track complex facial cues—including eye closure, drowsiness, and head pose—combining these signals for highly robust behavioral analysis.
    • Optimized the system for efficient CPU inference by implementing anti-flicker logic and no-face handling, ensuring smooth continuous monitoring during learning sessions.
    • Designed end-to-end data analytics pipelines, utilizing Python (NumPy, Pandas, Matplotlib) to perform Exploratory Data Analysis (EDA) and visualize student focus trends and attention patterns.
    • Applied advanced mathematics and statistical modeling concepts to train, evaluate, and fine-tune machine learning and deep learning models using TensorFlow, Keras, and Scikit-learn.

BLACKBUCK ENGINEERS PVT. LTD.

ChatGPT, Prompt Engineering and Generative AI Intern

May 2025 – July 2025 (120 Hours)
Key Achievements & Learning:
  • Built and deployed a Generative AI-Based Interview Preparation Chatbot on Streamlit Cloud, creating an end-to-end interactive application that demonstrates full-stack AI integration.
  • Leveraged OpenAI and Gemini APIs with Python to analyze uploaded resumes (PDF/DOCX) and dynamically generate context-aware, role-specific interview questions.
  • Engineered advanced prompt engineering techniques—including zero-shot, few-shot, chain-of-thought, and prompt chaining—to optimize multi-turn conversations, manage token limitations, and prevent LLM hallucinations.
  • Implemented Natural Language Processing (NLP) pipelines using NLTK and Scikit-learn for semantic text analysis and intelligent keyword extraction from candidate profiles.
  • Designed an automated response evaluation system utilizing the STAR methodology and structured prompting to assess candidate answers and deliver personalized, real-time feedback.
  • Demonstrated deep understanding of LLM behavior and AI-powered automation, bridging raw model capabilities with real-world decision support systems.
  • Tools & Technologies: Python, OpenAI API, GPT-4, Gemini (LLM), NLP (NLTK), Scikit-learn, Streamlit, Flask, Jupyter Notebook, JSON.

NIELIT (National Institute of Electronics & Information Technology)

Artificial Intelligence and Machine Learning Intern

June 2024 – July 2024 (8 Weeks)
Key Achievements & Learning:
  • Gained hands-on experience in developing and evaluating machine learning and deep learning models using Python
  • Implemented supervised learning algorithms including KNN, Decision Tree, and Support Vector Machine (SVM)
  • Applied unsupervised techniques such as K-Means, Fuzzy C-Means, and DBSCAN for clustering tasks
  • Performed dimensionality reduction using Principal Component Analysis (PCA)
  • Built and trained Artificial Neural Networks (ANN) with Backpropagation and Convolutional Neural Networks (CNN)
  • Developed a Heart Attack Prediction model with performance evaluation based on classification metrics
  • Utilized Tools and Libraries: Scikit-learn, Keras, NumPy, Pandas, Matplotlib, Seaborn

Featured Projects

Ongoing Project
AI Product Research Agent project photo

AGENTIC AI-BASED PRODUCT ANALYSIS SYSTEM

Developing an autonomous Agentic AI system using LangGraph and RAG to evaluate e-commerce products across Amazon and Flipkart. Combines multi-agent orchestration, review sentiment analysis, and historical price analytics to deliver evidence-grounded buying recommendations.

Key Features:
  • Architecting an autonomous agentic workflow with Python, LangGraph, and LangChain to coordinate multi-step product research, web scraping, and data synthesis.
  • Building an end-to-end RAG pipeline using FAISS/ChromaDB vector stores to ingest customer reviews and specifications for context-aware, grounded QA.
  • Implementing specialized Review & Comparison Agents to perform sentiment extraction, identify key pros/cons, and track cross-platform price differentials in real time.
  • Designing a predictive price analytics module that evaluates historical pricing trends and market data to output automated BUY / WAIT / AVOID guidance.
  • Developing a responsive web app using FastAPI and Streamlit, with ongoing preparation for Google Cloud Platform (GCP) deployment.
Python LangGraph LangChain Agentic AI RAG FAISS ChromaDB FastAPI Streamlit Price Analytics Google Cloud LLMs
GenAI Multilingual Chatbot photo

PRODUCTION-GRADE GENAI MULTILINGUAL CHATBOT WITH OBSERVABILITY

Architected an enterprise-ready conversational AI platform leveraging Groq LPU acceleration and LangChain LCEL for high-throughput, ultra-low-latency inference. Integrated full-stack LLM observability via LangSmith to audit pipeline execution, latency metrics, and token usage in real-time.

Key Features:
  • Engineered a high-speed inference engine using Groq LPUs and LangChain (LCEL), delivering sub-second response times using open-weights models like GPT-OSS 120B.
  • Implemented end-to-end LLM Observability & Telemetry with LangSmith, enabling real-time execution tracing, latency breakdown, token cost tracking, and prompt debugging.
  • Architected an adaptive multilingual context engine supporting multi-turn conversational state persistence across languages including English, Hindi, Telugu, Spanish, and French.
  • Designed production-ready dual frontends: a sleek, responsive Flask + Tailwind CSS REST API web app with dynamic fetch rendering, alongside a lightweight Streamlit dashboard.
  • Enforced enterprise-grade security and deployment standards using Gunicorn WSGI server, strict environment secret isolation (.env / .gitignore), and cloud-ready configuration on Render and Streamlit Cloud.
Python Groq API LangChain (LCEL) LangSmith LLM Observability Flask Tailwind CSS Streamlit REST APIs Prompt Engineering Gunicorn LLMs
Interview ChatBot photo

GENERATIVE AI-BASED INTERVIEW PREPARATION CHATBOT

Built a AI interview assistant using Gemini API and NLP to analyze resumes (PDF/DOCX) and generate context-aware questions. Implemented keyword extraction with NLTK & Scikit-learn and answer evaluation using STAR methodology for personalized feedback.

Key Features:
  • Engineered a Generative AI interview assistant using Python, Gemini API, and OpenAI API to parse PDF/DOCX resumes and dynamically generate context-aware, role-tailored questions.
  • Implemented robust NLP pipelines with NLTK and Scikit-learn for profile feature extraction, tailoring evaluation logic to match candidate skills and experience levels.
  • Designed an automated response evaluation engine based on the STAR methodology (Situation, Task, Action, Result) and keyword scoring to deliver real-time, actionable performance feedback.
  • Architected an interactive multi-turn conversational system utilizing advanced prompt engineering (chain-of-thought, zero-shot/few-shot) to optimize response context and reduce model hallucinations.
  • Deployed live on Streamlit Cloud, providing a seamless web interface for real-time interaction, resume uploads, and dynamic assessment workflows.
Python OpenAI API Gemini API NLP NLTK Scikit-learn Streamlit Flask Pandas NumPy PyPDF2 python-docx Prompt Engineering LLMs
Student Attention Monitoring photo

Student Attention Monitoring and Analysis System

Designed a real-time student engagement detection system using MobileNetV2, achieving 91% accuracy. Combined Model outputs with eye-closure and head-pose signals for robustness, and optimized CPU inference with anti-flicker and no-face handling.

Key Features:
  • Engineered a real-time computer vision system leveraging MobileNetV2 and CNNs to track multi-signal facial cues (eye movement, head pose, drowsiness) and classify student engagement with 91% accuracy.
  • Integrated OpenCV video stream processing to execute live camera feed analysis for continuous, low-latency detection of inattentive behavior.
  • Optimized pipeline efficiency for CPU inference by implementing custom anti-flicker logic and edge-case handling for frame dropouts and missing faces.
  • Deployed and monitored on Google Cloud Platform (GCP) to enable scalable cloud infrastructure and continuous performance tracking.
  • Built automated EDA and visualization pipelines using NumPy, Pandas, and Matplotlib to analyze focus patterns and derive actionable engagement trends across learning sessions.
Python OpenCV TensorFlow Keras CNN MobileNetV2 NumPy Pandas Matplotlib Computer Vision Image Processing Real-time Video Analysis

Technical Skills

Programming Languages

Python C/C++ Java R HTML CSS JavaScript

Libraries & Frameworks

LangChain LangGraph Hugging Face TensorFlow PyTorch Keras Scikit-Learn Pandas NumPy Matplotlib Seaborn NLTK FastAPI Flask Streamlit OpenCV

Tools & Platforms

Vector Databases (FAISS, Pinecone) OpenAI API Git/GitHub AWS GCP Jupyter Notebook VS Code Google Colab Linux Tableau ChatGPT Gemini Claude AI

AI/ML Techniques

Agentic AI Workflows Multi-Agent Systems Generative AI LLMs RAG (Retrieval-Augmented Generation) Prompt Engineering Machine Learning Deep Learning NLP Computer Vision Probability and Statistics

Certifications

Agentic AI Certified Foundations Associate

Oracle University

Agents Course

Hugging Face

Programming, Data Structures And Algorithms Using Python

NPTEL

Data Science for Engineers

NPTEL

Foundation of Cloud IoT Edge ML

NPTEL

Introduction to Data Science with Python

HARVARDX - EDX

Machine Learning with Python

IBM

SQL and Relational Databases

IBM

Participations

Smart India Hackathon (SIH)

Participated in Smart India Hackathon (SIH) by developing an Intrusion Prevention System for real-time DDoS/DoS attack mitigation, leveraging machine learning for anomaly detection and network security.

National Level Hackathon

Wayspire Workshop – Beyond Prompt Engineering

Attended a 2-day intensive workshop on "Beyond Prompt Engineering: Unlocking the GenAI Lab", gaining hands-on experience with advanced prompt design, LLM capabilities, and real-world Generative AI applications.

Get In Touch

Let's Connect

I'm always interested in discussing new opportunities, innovative projects and collaborations in the field of Artificial Intelligence and Machine Learning.

Email

sgreddy008@gmail.com

Phone

+91 9032294270

Location

10-60, Vempati Bazar
Rentachintala, Palnadu District
Andhra Pradesh - 522421