Core Focus

• Scalable cloud-native Data & AI platforms (AWS / Azure / Databricks)

• Production MLOps & LLMOps (CI/CD, evaluation, governance, observability)

• Retrieval + GenAI integration (RAG pipelines, embedding quality, latency)

• Data quality, lineage, reproducibility & cost efficiency

Selected Projects

• Awesome LLM Apps – curated LLM use cases (RAG, multi-agent): https://github.com/amineHY/awesome-llm-apps

• assimilate_llm – experimentation harness for LLM workflows & evaluation: https://github.com/amineHY/assimilate_llm

• rag_langchain / rag_deeplearning_ai – comparative retrieval pipeline patterns: https://github.com/amineHY/rag_langchain

• semantic_search_mongodb – vector + semantic search over operational data: https://github.com/amineHY/semantic_search_mongodb

• auto_data_scientist – automation of routine data science tasks: https://github.com/amineHY/auto_data_scientist

Tech Stack (Condensed)

Python, Spark, Delta Lake, Databricks, MLflow, PyTorch, TensorFlow, Terraform, GitHub Actions, Docker, PostgreSQL, MongoDB, Redis.

Mentoring & Community

• Trained 50+ consultants (Spark & Databricks enablement)

• Coached 20+ data scientists / ML engineers

• Talks: cloud data platforms, MLOps, LLMOps, retrieval optimization

• OSS experimentation: RAG, semantic search, multi-agent orchestration

How I Work

• Pragmatic architecture

• Shift-left governance

• Deterministic builds

• Automation-first

• Measured performance & drift

• Continuous enablement

Publications (Selected)

• Detection of epileptics during seizure free periods

• Convolution kernels for multi-wavelength imaging

• Spatio-Spectral Multichannel Reconstruction from few Low-Resolution Multispectral Data

• Restoration of astrophysical objects from multispectral data

Certifications

• Neural Networks and Deep Learning

• Improving Deep Neural Networks

• Workshop on System and Signal Processing and their Applications

GitHub Stats

Contact

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