Generative AI · Machine Learning · Deep Learning · Software Engineering
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I'm a Generative AI and Machine Learning Engineer at Litmus7, with four years of experience and a background in computer science.
My work and interests span machine learning, deep learning, fine-tuning, self-hosted model serving, and software engineering. I'm interested in the full journey from experimenting with models to building the software and infrastructure that make them useful.
- 🧠 AI & ML: Machine learning, deep learning, and generative AI.
- 🔧 Fine-tuning: Adapting pretrained models to specific tasks and domains.
- 🖥️ Self-hosted serving: Running models on infrastructure I control and exploring inference and deployment.
- ⚙️ Software engineering: Building maintainable applications, APIs, and automation around AI systems.
- 🌱 Currently learning: Agentic AI and Infrastructure as Code (IaC).
| Area | Technologies & Focus |
|---|---|
| Languages & scripting | Python, TypeScript, Bash |
| Machine learning & deep learning | PyTorch, TensorFlow, Keras, scikit-learn |
| Generative AI | Retrieval-Augmented Generation (RAG), fine-tuning, model serving |
| Model serving | vLLM, self-hosted inference |
| Agent frameworks | LangGraph, AutoGen, Strands Agents |
| Data analysis & visualization | NumPy, pandas, Dask, Plotly |
| Cloud & infrastructure | AWS, Microsoft Azure, Terraform, self-hosted serving |
| Databases | PostgreSQL, SQLite, Neo4j |
| Software development & automation | Git, GitHub, Jenkins, Postman |
| Editors & other tools | Visual Studio Code, Vim, Markdown, Raspberry Pi, GitHub Pages |
- Agentic systems: How AI agents plan, use tools, and coordinate workflows.
- Model adaptation: Fine-tuning approaches and evaluating models for specific use cases.
- Self-hosted AI: Serving models with attention to performance, resource usage, and reliability.
- Infrastructure automation: Making environments easier to reproduce and manage through code.
Away from engineering, I enjoy PC gaming and football. Always up for a good game, a good match, or a conversation about what you're building.
Let's talk models, software, and self-hosted experiments.
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