Anjireddy AbbiReddy
erie,PA
Summary
AI/ML professional with practical experience in prompt engineering and evaluating large language models (LLMs) for phishing email classification in healthcare, finance, and retail. Expertise in utilizing advanced techniques such as chain-of-thought reasoning and zero/few-shot prompting with models like GPT-4 and Claude. Proficient in developing NLP pipelines using Python, TensorFlow, and PyTorch, focusing on data preprocessing and evaluation metrics. Committed to leveraging AI for cybersecurity and data automation through tools like Google Colab and API integrations.
Overview
2
2
years of professional experience
1
1
Certification
Work History
Prompt Engineering & LLM Evaluation
self research
Erie, PA
05.2025 - Current
- Synthetic Data Generation: Created 3,000 emails (1,000 per domain; 50% phishing) using Claude 3.5, simulating realistic threat patterns for the healthcare, financial, and retail sectors.
- Data Preprocessing: Cleaned and structured datasets by removing missing values and duplicates using Pandas; ensured high-quality inputs for modeling and LLM inference.
- Chain-of-Thought Prompt Engineering: Designed structured reasoning prompts to guide LLMs (GPT-4, DeepSeek, LLaMA, Gemini) for accurate phishing vs. legitimate classification.
- LLM-Based Evaluation: Benchmarked and compared model performance across domains using classification metrics—precision, recall, F1-score.
- Deep Learning Integration: Utilized TensorFlow and PyTorch for baseline model comparison and feature vector analysis alongside LLM outputs.
- Domain-Aware Threat Analysis: Identified the most effective model for each domain, and extracted domain-specific phishing indicators from classification results.
- Experimentation and Automation: Conducted all experiments in Google Colab, and automated workflows from data preprocessing to model inference and evaluation.
- Excel Reporting and Visualization: Generated automated XLSX reports and comparison charts (via XlsxWriter and Matplotlib) to visualize model accuracy and domain effectiveness.
AI/ML Engineer (LLM Focused)
Freelance / Academic Research
Erie, PA
01.2024 - 05.2025
- Prompt Engineering: Applied zero-shot, few-shot, and chain-of-thought (CoT) techniques to optimize large language model (LLM) performance for classification tasks.
- Email Content Analysis: Conducted in-depth analysis of email headers and bodies for phishing detection and natural language understanding.
- Large Language Model (LLM) Integration: Integrated and fine-tuned models including GPT-4o, Claude 3, DeepSeek, and LLaMA for NLP-based email classification.
- Chain-of-Thought Reasoning: Designed and implemented CoT prompting strategies to enhance model interpretability and accuracy in classification workflows.
- Model Evaluation: Assessed classification performance using key metrics such as Accurecy, precision, recall, and F1-score.
- Experimentation Environment: Utilized Google Colab for model testing, result tracking, and scalable experimentation in Python.
Education
Master of Science (M.S.) - Cyber Security
Gannon University
Erie, PA05-2025
Bachelor of Technology (B.Tech) - Aeronautical Engineering
JNTU Hyderabad
India01.2023
Skills
- Prompt engineering techniques
- Large language models expertise
- Natural language processing
- Synthetic data generation
- Model evaluation metrics
- TensorFlow and PyTorch proficiency
- Data manipulation with Pandas
- Numerical analysis with NumPy
- Google Colab usage
- API integration skills
- Excel automation tools
- Version control with Git
Certification
- DeepLearning.AI - Prompt Engineering for ChatGPT
-introduction to large language models by google cloud
-Start writing prompt like a pro by google
Timeline
Prompt Engineering & LLM Evaluation
self research
05.2025 - Current
AI/ML Engineer (LLM Focused)
Freelance / Academic Research
01.2024 - 05.2025
Master of Science (M.S.) - Cyber Security
Gannon University
Bachelor of Technology (B.Tech) - Aeronautical Engineering
JNTU Hyderabad
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