Machine Learning Engineer
Specializing in Applied Machine Learning, LLM Infrastructure, and Low-Latency Inference Systems
- Foundation Models & LLM Systems: Fine-tuning (PEFT/LoRA), alignment pipelines, RAG with reranking architectures, and evaluation suites.
- Applied ML & Deep Learning: Vision, sequence modeling, transformer architectures, and representation learning.
- Inference Optimization & MLOps: Quantized runtimes (AWQ, GPTQ), model serving via vLLM and TensorRT-LLM, CI/CD pipeline tracking, and containerization.
| Domain | Stack & Frameworks |
|---|---|
| Languages | Python, C++, SQL, Bash |
| Deep Learning & Modeling | PyTorch, Hugging Face, DeepSpeed, ONNX |
| Data Processing & ML | NumPy, Pandas, Polars, Scikit-learn |
| Serving & Infrastructure | vLLM, FastAPI, Docker, Triton Inference Server, Ray |
| Vector Indexing & Storage | Qdrant, ChromaDB, FAISS |
| Tracking & Experimentation | Weights & Biases, MLflow |
I am currently working on this section. New production implementations, benchmark studies, and open-weights model repositories are being prepared for public release.
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