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β MUKHAMMADKODIR ABDUSALOMOV Β· AI ENGINEER Β· DATA SCIENTIST β
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class Engineer:
name = "Mukhammadkodir Abdusalomov"
location = "Warsaw, Poland"
role = "Command Center AI Engineer @ Procter & Gamble"
education = [
"M.S. Data Science & Business Analytics β University of Warsaw (GPA 4.2/5.0)",
"M.S. Business Analytics β Maastricht University, NL (GPA 8.0/10.0, Exchange)",
"B.S. Computer Science β University of ΕΓ³dΕΊ (GPA 4.0/5.0)",
]
focus = ["Generative AI", "RAG Systems", "MLOps", "LLM Engineering"]
languages = ["English C1", "Russian C1", "Polish A2", "Dutch A1"]Architecting production-grade RAG systems and low-latency analytical engines.
End-to-end AI: ETL pipelines in Databricks β LLM-powered APIs on Azure/GCP.
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Procter & Gamble Β· Warsaw, Poland
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Infosys Poland Β· ΕΓ³dΕΊ, Poland
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AI / ML
Data & Analytics
Cloud & Infrastructure
Databases
Stack: Azure Β· FastAPI Β· Gemini 3 Flash Β· Python Β· Ngrok
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β Dubai Real Estate Data β
β β β
β βΌ β
β [Custom Training Pipeline] βββΊ Feature Engineering β
β β + Outlier Detection β
β βΌ β
β [High-Precision Regression Engine on Azure] β
β β β
β βΌ β
β [FastAPI + Ngrok] βββΊ Gemini 3 Flash Conversational Layer β
β Zero/Few-Shot Prompt Engineering β
β Natural Language Predictions β
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- Architected custom training pipeline for high-dimensional real estate data
- Implemented advanced feature engineering & outlier detection to optimize model loss
- Operationalized Gemini 3 Flash conversational layer via FastAPI
- Engineered Zero/Few-shot prompts to mitigate prompt injection risks
NLP / GenAI ββββββββββββββββββββ LLMs, RAG, Embeddings, Transformers
Machine Learning ββββββββββββββββββββ Regression, Classification, Ensembles
Deep Learning ββββββββββββββββββββ CNNs, RNNs, Transformers
Time Series ββββββββββββββββββββ ARIMA, LSTM, Forecasting
Statistics / Econometrics ββββββββββββββββββ Inference, Panel Data
MLOps / Cloud ββββββββββββββββββββ Azure, GCP, Databricks, FastAPI
"Transforming high-velocity data into scalable, cloud-native intelligence."