Machine Learning Engineer | LLM & Agentic AI | RAG | Applied ML | Engineering AI
I build practical AI systems spanning agentic workflows, retrieval-augmented generation, machine learning, GPU computing, engineering applications, and AI education.
An agentic course-development and quality-assurance system built with Strands Agents SDK and AWS.
- Requirements extraction and constraint analysis
- Agent-based course planning
- Deterministic validation
- Instructor-in-the-loop review
- AWS Bedrock integration
A comparative implementation of course-intelligence workflows using native Python and multiple agent frameworks.
Areas explored include:
- Agent roles and orchestration
- Research and summarization
- Validation
- Multi-agent workflow design
- Framework comparison
An end-to-end retrieval-augmented generation application using LLMs, vector retrieval, and contextual evidence.
Experiments with Llama fine-tuning using QLoRA and Unsloth, with emphasis on efficient model adaptation.
Applications of AI and machine learning to engineering problems, including predictive maintenance, computer vision, structural health monitoring, HVAC optimization, and edge/cloud deployment.
Hands-on work with:
- CUDA C/C++
- GPU memory management
- CUDA streams
- Thrust
- GPU image processing
- ONNX
- TensorRT
- AI inference acceleration
Additional work includes:
- Databricks
- Data pipelines
- Machine learning
- XGBoost
- Statistical analysis
- Data visualization
- Reproducible analytics
I develop and teach university-level courses and learning materials in areas including:
- Artificial Intelligence
- Data Science and Analytics
- Object-Oriented Programming
- Machine Learning
- LLM and Agentic AI
- GPU Computing
- Engineering applications of AI
Agentic AI · Multi-Agent Systems · LLM Evaluation · RAG · AI Engineering · GPU Computing
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