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.
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Scalable cloud-native Data & AI platforms (AWS / Azure / Databricks)
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Production MLOps & LLMOps (CI/CD, evaluation, governance, observability)
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Retrieval + GenAI integration (RAG pipelines, embedding quality, latency)
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Data quality, lineage, reproducibility & cost efficiency
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Awesome LLM Apps – curated LLM use cases (RAG, multi-agent): https://github.com/amineHY/awesome-llm-apps
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assimilate_llm – experimentation harness for LLM workflows & evaluation: https://github.com/amineHY/assimilate_llm
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rag_langchain / rag_deeplearning_ai – comparative retrieval pipeline patterns: https://github.com/amineHY/rag_langchain
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semantic_search_mongodb – vector + semantic search over operational data: https://github.com/amineHY/semantic_search_mongodb
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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.
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Trained 50+ consultants (Spark & Databricks enablement)
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Coached 20+ data scientists / ML engineers
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Talks: cloud data platforms, MLOps, LLMOps, retrieval optimization
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OSS experimentation: RAG, semantic search, multi-agent orchestration
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Pragmatic architecture
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Shift-left governance
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Deterministic builds
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Automation-first
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Measured performance & drift
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Continuous enablement
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Detection of epileptics during seizure free periods
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Convolution kernels for multi-wavelength imaging
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Spatio-Spectral Multichannel Reconstruction from few Low-Resolution Multispectral Data
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Restoration of astrophysical objects from multispectral data
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Neural Networks and Deep Learning
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Improving Deep Neural Networks
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Workshop on System and Signal Processing and their Applications
