Professional Experience

Lead Data Engineer / MLOps Engineer — Time-Series & Streaming Systems

MOcons GmbH & Co. KG | Hochschule Ruhr West

  • Designed and engineered a core backend microservice infrastructure for real-time time-series anomaly detection and clustering deployed for major German utility and environmental organizations including StEB Köln, RWW, LANUV NRW, EGLV, and Bitcontrol.
  • Designed and implemented a Kafka / Kafka Streams event-driven streaming architecture processing up to 1,100 concurrent time-series streams with sampling frequencies from 1 to 15 minutes.
  • Built scalable pipelines for real-time ingestion, historical reprocessing, and long-term backfilling (up to 15 years of data).
  • Implemented ML lifecycle management and model tracking using MLflow.
  • Designed hybrid orchestration systems to manage decoupled streaming and batch data workloads.

Data Scientist & ML Engineer

Snappfood | Oct 2023 – Feb 2025

  • Developed LLM-based systems for customer support automation and CRM enhancement.
  • Built retrieval-augmented generation (RAG) pipelines for recommendation and query optimization.
  • Applied NLP techniques for intent extraction and behavioral modeling.
  • Fine-tuned transformer models using domain-specific instruction datasets.

ML Researcher & Developer

Friedrich Schiller University Jena | Jan 2019 – Sep 2023

  • Developed deep learning models for cross-domain text and image analysis.
  • Designed transformer-based architectures for document classification, clustering, and structural modeling.
  • Conducted research in long-range dependency modeling and time-series behavior in textual data.
  • Built reproducible ML experimentation pipelines.

ML Researcher & Developer

Heidelberg Institute for Theoretical Studies (HITS) | Jan 2018 – Dec 2018

  • Designed LSTM and Tree-LSTM architectures for sequence modeling.
  • Developed generative models for abstractive summarization.
  • Applied reinforcement learning to neural sequence optimization.

ML Engineer / Team Lead — NLP Systems

Intelligent Information System Lab | Sep 2016 – Dec 2017

  • Led development of a Named Entity Recognition (NER) system as a fundamental module of a Persian Search Engine.
  • Coordinated research and engineering teams for NLP pipelines.
  • Built Java-based production NLP components for Persian search systems.

Developer & ML Engineer

Mobin Information Technology Research Center | Jan 2014 – Aug 2016

  • Built NLP pipelines for entity recognition and relation extraction.
  • Developed clustering and recommendation systems for large-scale news text data.

Computational Linguistics Developer

University of Tehran — NLP Lab | May 2008 – Sep 2013

  • Developed foundational NLP modules (tokenization, POS tagging, parsing).
  • Built semi-supervised annotation frameworks for linguistic datasets.

Education

PhD in Computational Language Processing

Friedrich Schiller University Jena | 2024

  • Project: Structural analysis of long-form text using machine learning, deep learning, and time-series methods

MSc in Artificial Intelligence and Robotics

Iran University of Science and Technology | 2008

  • Thesis: Development of core NLP modules (POS tagging, parsing, named entity recognition) for analyzing Persian texts

Certifications

Microsoft Azure

  • Microsoft Certified: Azure AI Engineer Associate (2026)
  • Microsoft Certified: Azure Developer Associate (2026)
  • Microsoft Certified: Azure Data Scientist Associate (2025)
  • Microsoft Certified: Azure AI Fundamentals (2024)

Data / Orchestration

  • Astronomer Certification: Apache Airflow 3 Fundamentals (2026)

Technical Skills

Machine Learning & AI

  • Python
  • PyTorch
  • TensorFlow
  • Microsoft Foundry
  • Hugging Face Transformers
  • scikit-learn
  • fastai
  • LangChain
  • LangGraph

MLOps & Orchestration

  • Azure ML
  • MLflow
  • Airflow
  • Docker

Data Engineering & Streaming

  • Apache Kafka
  • Kafka Streams
  • Apache Spark
  • PySpark

Cloud

  • Microsoft Azure

Databases

  • PostgreSQL
  • MongoDB
  • MySQL

Selected Publications

  • Comparative Analysis of Preference in Contemporary and Earlier Texts Using Entropy Measures (Entropy, 2023)
  • Approximate Entropy in Canonical and Non-Canonical Fiction (Entropy, 2022)
  • Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts (Frontiers in Psychology, 2021)
  • Global Image Properties Predict Ratings of Affective Pictures (Frontiers in Psychology, 2020)
  • PEYMA: Persian NER corpus (2019)

Hobbies

Traveling • Photography • Gardening