A technical lead and cloud/data platforms architect focused on building reliable, observable, cost-aware AI & analytics foundations (MLOps + LLMOps + data engineering). I help teams ship production ML and GenAI responsibly.
Fast glance: Core focus, projects, stack, mentoring, stats, contact.
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
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
Python, Spark, Delta Lake, Databricks, MLflow, PyTorch, TensorFlow, Terraform, GitHub Actions, Docker, PostgreSQL, MongoDB, Redis.
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
Pragmatic architecture
Shift-left governance
Deterministic builds
Automation-first
Measured performance & drift
Continuous enablement
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
Neural Networks and Deep Learning
Improving Deep Neural Networks
Workshop on System and Signal Processing and their Applications
