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AI / ML Engineer · Computer Vision · NLP · Full-Stack Systems

Likhith Satya Neerukonda

Computer Science graduate student at the University of North Texas with internship and project experience in software development, AI/ML, and data-driven applications. Built and deployed real-time computer vision and NLP features using Python, PyTorch, TensorFlow, YOLOv8, BERT, AWS SageMaker, Flask, Streamlit, and PostgreSQL. Seeking entry-level software engineering, AI/ML, or full-stack roles.

98% detection accuracy using YOLOv8 + ResNet on AWS SageMaker
40% faster image processing through optimized ML inference pipelines
20% user satisfaction gain through BERT-based feedback analysis
Download Resume View Projects Email Me LinkedIn GitHub

professional_summary

Computer Science graduate building practical AI/ML and full-stack software.

Computer Science graduate student at the University of North Texas with internship and project experience in software development, AI/ML, and data-driven applications. Built and deployed real-time computer vision and NLP features using Python, PyTorch, TensorFlow, YOLOv8, BERT, AWS SageMaker, Flask, Streamlit, and PostgreSQL.

Comfortable working end-to-end across data pipelines, model development, and backend applications; seeking entry-level software engineering, AI/ML, or full-stack roles.

technical_stack

Grouped by how I build intelligent systems.

My stack centers on machine learning, computer vision, NLP, backend engineering, cloud deployment, and data tooling used across research and production-style projects.

01 / Machine Learning & AI

PyTorch TensorFlow scikit-learn YOLOv7 YOLOv8 ResNet BERT NLP Computer Vision

02 / Languages

Python C++ JavaScript Ruby

03 / Frameworks & Tools

Flask Streamlit Ruby on Rails Docker Git

04 / Data & Cloud

PostgreSQL SQL MongoDB BigQuery AWS S3 SageMaker Google Cloud Platform (GCP)

05 / Visualization & Reporting

Power BI Streamlit dashboards LaTeX reports

experience_timeline

AI systems, automation pipelines, and applied research.

My experience spans campus workflow automation, applied machine learning in product environments, and research-driven experimentation in networking and software systems.

Student Assistant

University of North Texas · Denton, Texas

Feb 2025 – Feb 2026

  • Wrote Python scripts to automate form validation and feedback summarization, reducing processing time and manual errors.
  • Managed scheduling and communication using Outlook, Slack, and Google Sheets to keep meetings and resources organized.
Python Automation Outlook Slack

AI/ML & Full-Stack Developer Intern

Prasthana Software Solutions

Jun 2023 – May 2024

  • Developed onboarding features with Python, Flask, and PostgreSQL that streamlined the user registration flow and reduced friction during sign-up.
  • Built and deployed a real-time image detection system using YOLOv8, ResNet, and AWS SageMaker that achieved 98% accuracy and improved processing speed by 40%.
  • Created Python data-preprocessing pipelines using pandas and scikit-learn that automated image preparation, cutting manual effort and speeding up model training.
  • Built a sentiment analysis feature using BERT and Streamlit to identify user feedback trends and improve user satisfaction by 20%.
  • Participated in Agile sprints, code reviews, and team discussions to support faster, more reliable feature delivery.
Python Flask PostgreSQL YOLOv8 ResNet AWS SageMaker BERT Streamlit

Mobile Ad Hoc QoS Researcher

Amrita Vishwa Vidyapeetham · Chennai, India

Apr 2023 – Jun 2023

  • Ran MANET QoS experiments using NS-3, PyTorch, and Docker, improving network efficiency in the tested scenarios.
  • Analyzed routing, mobility, and packet flow using Python scripts and simulations, identifying key inefficiencies that informed protocol-improvement recommendations.
  • Reviewed recent research papers and documented key findings, giving the team a concise reference that informed our QoS testing approach.
  • Prepared LaTeX reports and presented results to the research team, helping guide decisions on follow-up experiments and focus areas.
NS-3 Docker PyTorch Research LaTeX

Software Engineering Researcher

Research Project

Oct 2022 – Apr 2023

  • Built automation tools with Flask and PyTorch to reduce repetitive tasks.
  • Helped organize research workflows and project milestones with the team so progress and deadlines were easier to track.
  • Used Git for version control and documented weekly progress updates to keep the team aligned on code changes and project status.
Flask PyTorch Git Automation

featured_projects

Technical projects with practical machine learning outcomes.

These projects reflect the kind of work I want to do professionally: real-time inference, intelligent automation, and systems that bridge research and deployment.

Computer Vision

IntelliFace – AI Attendance System

Built a face-recognition attendance platform that combines YOLOv8-based detection with ResNet encodings for real-time identification and automated logging.

  • Implemented live webcam-based face detection and user verification for real-time attendance capture.
  • Automated attendance logging workflows to reduce manual tracking effort and improve reliability.
  • Built Streamlit and Flask interfaces so users can monitor attendance data through simple dashboards.
YOLOv8 ResNet OpenCV Flask Streamlit

Smart Mobility / Computer Vision

Smart Curb Parking System

Designed an automated parking enforcement system using deep learning and image preprocessing to detect curbside violations from real-time camera streams.

  • Used YOLOv7 and ILPD-NET to identify vehicles and violation patterns from live or recorded feeds.
  • Applied edge detection and Gaussian filtering to strengthen detection robustness in varied lighting conditions.
  • Built preprocessing and inference workflows suitable for real-time monitoring and reduced manual supervision.
YOLOv7 ILPD-NET Image Processing Python OpenCV

what I enjoy building

Real-time ML systems

Detection, recognition, and inference pipelines that run on images, video, or structured data.

Applied NLP

Text classification, sentiment analysis, and workflow automation that turns raw feedback into actionable insights.

Research to deployment

I like connecting experimentation, analysis, and software delivery instead of treating them as separate tracks.

education

Computer science foundation with AI-focused coursework.

M.S. in Computer Science

University of North Texas · Denton, Texas

Aug 2024 – May 2026 · GPA: 3.7

Artificial Intelligence Machine Learning Big Data Software Engineering Advanced Algorithms Cloud Computing Software Development for AI

B.Tech in Computer Science

Amrita School of Engineering · Chennai, India

Aug 2020 – May 2024 · GPA: 7.17

Data Structures and Algorithms Operating Systems OOP Distributed Systems DBMS Web Technologies Artificial Intelligence Machine Learning Cloud Computing

certifications

Google Cloud Computing Certification
Microsoft: Design and Manage Analytics Solutions Using Power BI (PL-300) — In progress
AWS Solutions Architecture Virtual Experience (Forage)
Accenture Developer Virtual Experience (Forage)
C++ Course (Udemy)

return ready_to_collaborate;

Let’s build practical AI systems.

I’m currently open to AI/ML Engineer, Computer Vision Engineer, NLP Engineer, and Data Scientist opportunities where I can contribute to real products, applied research, or intelligent automation systems.

Denton, Texas • Likhithsatya2003@gmail.com • (940) 594-1279
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