About Me
I specialize in prompt engineering, LLM reasoning, and intelligent automation. My work focuses on building AI systems that combine structured prompting, retrieval-augmented generation, and Python automation to deliver real-world impact. I bring a strong foundation in machine learning, deep learning, and data engineering, supported by a comprehensive 24-week AI curriculum.
Skills Snapshot
- Prompt Engineering
- LLM Reasoning
- Python Automation
- RAG Pipelines
- AI Safety & Evaluation
- Machine Learning Fundamentals
Projects
Personal AI Assistant with Memory
Description: A multi-turn conversational assistant with memory, tool use, and safety layers.
Skills: Prompt orchestration, system prompts, tool integration, safety prompting
Tech: Python, OpenAI API, LangChain
LLM-Powered Data Analyst
Description: Upload a CSV and receive automated insights, SQL queries, and charts.
Skills: Structured prompting, reasoning, data analysis
Tech: Python, Pandas, Streamlit
Document QA System (RAG Pipeline)
Description: Embeddings + vector search + context-aware prompting for grounded answers.
Skills: Retrieval-augmented generation, embeddings, vector databases
Tech: FAISS or Pinecone, Python, Transformers
Prompt Pattern Library
Description: A curated library of 20–30 prompt patterns with examples and use cases.
Skills: Prompt design, evaluation, documentation
Tech: Markdown, GitHub Pages (or static site generator)
Automated Workflow Engine (Python + LLM)
Description: LLM-powered automation for summarization, classification, and reporting.
Skills: Python automation, applied prompting
Tech: Python, FastAPI (or similar framework)
LLM Safety & Evaluation Suite
Description: A suite of adversarial prompts, test prompts, and evaluation metrics.
Skills: Safety prompting, evaluation, red-teaming
Tech: Python, OpenAI API
Skills
Core Prompt Engineering Skills
- Prompt patterns (Chain-of-Thought, ReAct, RAG, self-critique)
- LLM reasoning & structured output design
- Safety prompting & evaluation
- Retrieval-augmented generation (RAG)
- Adversarial prompt testing and red-teaming
Technical Skills
- Python
- Pandas, NumPy
- Scikit-Learn, PyTorch
- FastAPI, Docker
- Vector databases (FAISS, Pinecone, Chroma)
- Cloud fundamentals (AWS, Azure, GCP)
Tools
- OpenAI API
- Azure OpenAI
- LangChain
- Git & GitHub
24-Week AI & Prompt Engineering Curriculum
Week 1 — Prompting Basics + Intro Python
Books
- The Art of Prompt Engineering with ChatGPT — Nathan Hunter
- Automate the Boring Stuff with Python — Al Sweigart (Ch. 1–3)
YouTube
- DeepLearning.AI — ChatGPT Prompt Engineering for Developers
- Programming with Mosh — Python Tutorial for Beginners
Week 2 — Prompt Patterns + Python Fundamentals
Books
- The Art of Prompt Engineering (Prompt patterns section)
- Automate the Boring Stuff (Ch. 4–6)
YouTube
- Prompt Engineering Institute — Prompt Patterns
- freeCodeCamp — Python for Beginners
Week 3 — Pandas + NumPy
Books
- Python for Data Analysis — Wes McKinney (Ch. 1–5)
YouTube
- Corey Schafer — Pandas Tutorial Series
- freeCodeCamp — NumPy Tutorial
Week 4 — Applied Prompting + Small Python Projects
Books
- Automate the Boring Stuff (Project chapters)
YouTube
- Sentdex — Automating Tasks with Python
- DeepLearning.AI — Advanced Prompting Techniques
Week 5 — Intro to Machine Learning
Books
- Hands-On Machine Learning — Géron (Ch. 1–2)
- The Hundred-Page Machine Learning Book (Intro chapters)
YouTube
- Andrew Ng — Machine Learning Course (Week 1)
- StatQuest — What is Machine Learning
Week 6 — Regression & Classification
Books
- Hands-On ML (Ch. 3–4)
- Intro to ML with Python — Müller & Guido (Ch. 1–2)
YouTube
- StatQuest — Linear Regression
- StatQuest — Logistic Regression
- freeCodeCamp — Scikit-Learn Crash Course
Week 7 — Model Evaluation & Feature Engineering
Books
- Hands-On ML (Ch. 5–6)
YouTube
- StatQuest — Precision, Recall, F1
- StatQuest — ROC & AUC
- Krish Naik — Feature Engineering Tutorials
Week 8 — Unsupervised Learning + ML Project
Books
- Hands-On ML (Ch. 8 — Clustering)
YouTube
- StatQuest — K-Means Clustering
- StatQuest — PCA
- Corey Schafer — Project-Based ML Tutorials
Week 9 — Neural Network Fundamentals
Books
- Deep Learning — Goodfellow (Ch. 6)
- Neural Networks and Deep Learning — Nielsen (Ch. 1–2)
YouTube
- 3Blue1Brown — Neural Networks Series
- Sentdex — Neural Networks from Scratch
Week 10 — CNNs & Image Classification
Books
- Deep Learning with Python — Chollet (CNN chapters)
YouTube
- 3Blue1Brown — Convolutions
- freeCodeCamp — PyTorch for Deep Learning (CNN section)
Week 11 — RNNs, LSTMs, GRUs
Books
- Deep Learning — Goodfellow (Sequence modeling chapter)
YouTube
- StatQuest — RNNs, LSTMs Explained
- Sentdex — RNNs in PyTorch
Week 12 — Transformers & NLP
Books
- Deep Learning with Python (NLP chapter)
YouTube
- Jay Alammar — The Illustrated Transformer
- HuggingFace — Transformers Course Playlist
Week 13 — PyTorch/TensorFlow Training Pipelines
Books
- Deep Learning with Python (Training & tuning chapters)
YouTube
- freeCodeCamp — PyTorch Full Course
- freeCodeCamp — TensorFlow 2.0 Full Course
Week 14 — Deep Learning Project
Books
- No new books
YouTube
- Krish Naik — End-to-End DL Projects
- Patrick Loeber — Deploying ML Models
Week 15 — SQL & Data Modeling
Books
- Fundamentals of Data Engineering — Reis & Housley (Ch. 1–3)
YouTube
- freeCodeCamp — SQL Full Course
- Corey Schafer — SQL Tutorial
Week 16 — ETL/ELT Pipelines
Books
- Fundamentals of Data Engineering (Pipeline chapters)
YouTube
- DataTalksClub — Data Engineering Zoomcamp (ETL module)
- Seattle Data Guy — ETL Explained
Week 17 — Spark & Big Data Tools
Books
- Designing Data-Intensive Applications — Kleppmann (Distributed systems chapters)
YouTube
- freeCodeCamp — Apache Spark Full Course
- Databricks — Spark Essentials
Week 18 — Cloud Platforms & Data Warehouses
Books
- Fundamentals of Data Engineering (Cloud chapters)
YouTube
- Adam Marczak — Azure Data Engineering Tutorials
- Stephane Maarek — AWS Data Engineering
Week 19 — Streaming Systems & Kafka
Books
- Streaming Systems — Akidau
YouTube
- Confluent — Kafka Crash Course
- DataTalksClub — Streaming Module
Week 20 — Capstone Planning
Books
- Machine Learning Design Patterns — Lakshmanan (Planning chapters)
YouTube
- Google Cloud — MLOps Crash Course
Weeks 21–22 — Capstone Development
Books
- Building Machine Learning Pipelines — Hapke
YouTube
- Krish Naik — End-to-End ML Pipeline
- TechWorld with Nana — Docker for Beginners
Week 23 — Capstone Refinement
Books
- No new books
YouTube
- FastAPI — API Deployment Tutorials
- Patrick Loeber — Deploy ML Models to Cloud
Week 24 — Final Review & Portfolio Prep
Books
- No new books
YouTube
- Ken Jee — How to Build a Data Science Portfolio
- Alex The Analyst — Portfolio Projects
Resume
You can mirror the strong resume we built here. For now, this section can include:
Professional Summary
Driven AI practitioner specializing in prompt engineering, LLM reasoning, and intelligent automation. Skilled at designing high-impact prompts, building LLM-powered systems, and integrating AI into real-world workflows. Strong foundation in Python, machine learning, deep learning, and data engineering, backed by a structured 24-week AI curriculum.
Core Skills
- Prompt Engineering & LLM Reasoning
- Prompt Patterns (CoT, ReAct, RAG, Self-critique, Few-shot)
- Python Automation & Scripting
- Retrieval-Augmented Generation (RAG)
- LLM Evaluation, Safety & Guardrails
- API Integration (OpenAI, Azure, Anthropic)
- Embeddings & Vector Databases
- Machine Learning Fundamentals
- Deployment (FastAPI, Docker)
Training & Curriculum
Comprehensive 24-week AI Learning Curriculum covering: Prompt Engineering, Python, Machine Learning, Deep Learning, Data Engineering, and MLOps & Deployment.
Contact
- Email: [email protected]
- Phone: 847-346-6171
- Location: Trail Creek, Indiana
If you’d like to connect about opportunities, collaborations, or projects, feel free to reach out.