24.10.2026 aktualisiert
100 % verfügbar
AI/ML Engineer | Ph.D. in Computer Engineering | LangGraph, RAG & Financial Forecasting Specialist
Ardabil, Iran
Nur Remote
Ph.D. in Computer Engineering, Islamic Azad University of Qazvin (2021-2025)Über mich
AI/ML Engineer with a Ph.D. in Computer Engineering and 5+ years of experience in machine learning, deep learning, and reinforcement learning. Specialized in building agentic AI systems and Vibe Coding for financial market forecasting (Forex, gold, and equities).
Skills
AI AgentsMLOpsHTMLAngewandte WissenschaftCSSDatenbereinigungMachine LearningRecommendersystemeWordPressBestärkendes LernenDatenklassifizierungData ScienceLarge Language ModelsPrompt EngineeringDeep Learning
Machine Learning & Deep Learning
Expertise in machine learning, deep learning, and reinforcement learning using frameworks such as TensorFlow, Keras, XGBoost, CatBoost, Scikit-learn, and MATLAB Deep Learning Toolbox. Application of these techniques to financial market forecasting and academic research.
Agentic AI Systems & RAG Pipelines
Specialization in building production-grade agentic AI systems using LangGraph and LangChain, including Retrieval-Augmented Generation (RAG) pipelines, vector databases, and semantic search for real-time financial data processing.
Python & Data Engineering
Proficiency in Python with libraries such as NumPy, Pandas, and SQL for building end-to-end data pipelines, processing multi-modal financial data, and developing scalable AI architectures for real-time inference.
Natural Language Processing (NLP)
Application of NLP techniques including Large Language Models (LLM), Prompt Engineering, and semantic search to extract actionable insights from unstructured financial news and reports.
Reinforcement Learning
Application of reinforcement learning methods, including Q-Deep Learning Networks and OpenAI Gym, to complex problems such as friend recommendation systems and optimization tasks.
Time-Series Forecasting & Optimization
Development of time-series forecasting models and optimization algorithms for financial market prediction, including Forex, gold, and equity markets.
Tools & Platforms
Practical experience with Docker, Git/GitHub, MongoDB, n8n, Power BI, TA-Lib, yfinance, NetworkX, Django, and MS Office for development, deployment, and data visualization tasks.
Trading & Financial Analysis
Knowledge of Trading View, trading strategies, analysis of trading signals, stock market analysis, multi-chart view, and fundamental indicators for financial market applications.
Data Mining & Academic Research
Extensive background in data mining, with 16 peer-reviewed publications in international journals and conferences, covering topics such as recommendation systems, network security, anomaly detection, and IoT healthcare.
Expertise in machine learning, deep learning, and reinforcement learning using frameworks such as TensorFlow, Keras, XGBoost, CatBoost, Scikit-learn, and MATLAB Deep Learning Toolbox. Application of these techniques to financial market forecasting and academic research.
Agentic AI Systems & RAG Pipelines
Specialization in building production-grade agentic AI systems using LangGraph and LangChain, including Retrieval-Augmented Generation (RAG) pipelines, vector databases, and semantic search for real-time financial data processing.
Python & Data Engineering
Proficiency in Python with libraries such as NumPy, Pandas, and SQL for building end-to-end data pipelines, processing multi-modal financial data, and developing scalable AI architectures for real-time inference.
Natural Language Processing (NLP)
Application of NLP techniques including Large Language Models (LLM), Prompt Engineering, and semantic search to extract actionable insights from unstructured financial news and reports.
Reinforcement Learning
Application of reinforcement learning methods, including Q-Deep Learning Networks and OpenAI Gym, to complex problems such as friend recommendation systems and optimization tasks.
Time-Series Forecasting & Optimization
Development of time-series forecasting models and optimization algorithms for financial market prediction, including Forex, gold, and equity markets.
Tools & Platforms
Practical experience with Docker, Git/GitHub, MongoDB, n8n, Power BI, TA-Lib, yfinance, NetworkX, Django, and MS Office for development, deployment, and data visualization tasks.
Trading & Financial Analysis
Knowledge of Trading View, trading strategies, analysis of trading signals, stock market analysis, multi-chart view, and fundamental indicators for financial market applications.
Data Mining & Academic Research
Extensive background in data mining, with 16 peer-reviewed publications in international journals and conferences, covering topics such as recommendation systems, network security, anomaly detection, and IoT healthcare.
Erfahrung
Berufserfahrungmehr als 10 Jahre
Freelancer seit2016
Sprachen
EnglishverhandlungssicherPersianverhandlungssicherTurkishverhandlungssicher
Projekthistorie
Design of a production-grade agentic AI system using vibe coding for Forex and gold market forecasting using LangGraph-based workflows and advanced RAG pipelines. Integration of vector databases and semantic search to extract actionable insights from unstructured financial news and reports. Building end-to-end Python pipelines for real-time financial data ingestion, processing, and inference.
Lead and coordinate collaborative research projects in artificial intelligence, data mining, and optimization. Supervise junior researchers on research methodology, paper structuring, and publication standards. Manage timelines and research goals to support submission of peer-reviewed papers and conference presentations.
Collaboration on production-grade, AI-driven stock market prediction systems (SapienTrade.com), combining machine learning with NLP and RAG pipelines for investment decision support. Development of Python and SQL-based workflows to process multi-modal financial data and optimize semantic search for real-time market insights. Design and deployment of scalable AI architectures bridging complex financial forecasting with actionable, user-friendly intelligence.