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Presentation
AI Engineer and AI Workflow Architect with 3 years of experience designing, developing, and deploying AI-powered solutions, combined with 5 years of professional Python development experience. Specialized in building intelligent automation systems, AI agents, workflow orchestration platforms, predictive analytics solutions, and Human-in-the-Loop (HITL) AI systems. Experienced in integrating Large Language Models (LLMs), machine learning pipelines, APIs, cloud service,databases, and business automation frameworks to improve operational efficiency and decision-making.
• Design and develop AI-powered applications and intelligent automation systems.
• Build and deploy AI agents using LLMs and multi-agent architectures.
• Architect Human-in-the-Loop (Human + AI) operational workflows.
• Create AI workflow orchestration pipelines for business processes.
• Develop Retrieval-Augmented Generation (RAG) systems for enterprise knowledge
• Design and develop AI-powered applications and intelligent automation systems.
• Build and deploy AI agents using LLMs and multi-agent architectures.
• Architect Human-in-the-Loop (Human + AI) operational workflows.
• Create AI workflow orchestration pipelines for business processes.
• Develop Retrieval-Augmented Generation (RAG) systems for enterprise knowledge
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Background
Python Essentials for MLOps — Coursera
basics of Python testing. From a brief overview of the standard library to using a more modern approach with Pytest,work with data using Pandas and NumPy,create and use APIs with Python using HTTP and command-line tools,HTTP APIs to expose Machine Learning models.
Harnessing Ollama – Create Local LLMs with Python
Ollama's CLI commands and REST API to pull models , introduce you to various user interfaces for interacting with Ollama models, including a detailed look at the Msty app,Ollama Python library to build local LLM app,dive deep into vectorstores and embeddings for optimizing LLM applications,function calling within Ollama, creation of a final voice-enabled RAG system.
Advanced Learning Algorithms — DeepLearning.AI & Stanford University (Coursera) — 27 Nov 2025
neural networks and how to use them for classification tasks from scratch, train your model in TensorFlow, also other important activation functions (besides the sigmoid function), Multiclass classification, difference between multiclass classification and multi-label classification, Adam optimizer , machine learning lifecycle, tuning your model, and also improving your training data. decision tree, including random forests and boosted trees (XGBoost)
🇬🇧English 🇮🇷Persian
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Working hours
Monday
From08h00To18h00
Tuesday
From08h00To18h00
Wednesday
From08h00To18h00
Thursday
From08h00To18h00
Friday
From08h00To18h00
Saturday
Closed
Sunday â–ª Today
Closed
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