Meghdad Farahmand, Artificial Intelligence (AI) Developer in Berlin, Germany
Meghdad Farahmand

Artificial Intelligence (AI) Developer in Berlin, Germany

Member since June 6, 2019
Meghdad has more than 15 years of experience in AI, including developing computer vision models for traffic centers and manufacturers and developing SOTA NLP models for international clients such as Siemens, Unilever, and Allianz. He has a strong theoretical background that includes a bachelor's in computer engineering, a master's in computer science with a specialization in AI, and a Ph.D. in computer science with a specialization in NLP.
Meghdad is now available for hire


  • Merantix
    Python 3, Google Cloud Platform (GCP), PyTorch, Docker, Software Architecture...
  • omni:us
    Docker, Deep Learning, TensorFlow, PyTorch, Python
  • Market Logic Software
    Reinforcement Learning, Deep Learning, PyTorch, Python



Berlin, Germany



Preferred Environment

PyCharm, IntelliJ, Visual Studio Code, Linux, MacOS

The most amazing...

...project I’ve developed is a question-answering chatbot based on a BiDAF (bidirectional attention flow) model and a reinforcement learning-based dialog manager.


  • NLP Lead — Senior ML Engineer

    2021 - 2021
    • Consulted clients as well as internal stakeholders about NLP projects.
    • Developed some components and modules within the MLOps infrastructure.
    • Created a roadmap for developing NLP software components, packages, and pipelines concerning market signals.
    • Developed a label analysis package to evaluate the quality of the labels in labeled data.
    Technologies: Python 3, Google Cloud Platform (GCP), PyTorch, Docker, Software Architecture, Machine Learning Operations (MLOps), OOP Designs
  • Senior Data Scientist

    2018 - 2020
    • Developed and deployed a highly scalable and customizable document classification pipeline for multilabel and multiclass classification based on deep neural networks.
    • Studied and integrated transfer learning anomaly detection and multitask learning in order to improve the performance of the deployed classification and existing named entity recognition (NER) micro-services.
    • Carried out statistical, distributional, and syntactic analysis of textual data to improve downstream models' performance.
    • Co-advised M.Sc theses on NER and active learning.
    • Developed a text-to-text mapping service based on a siamese neural network.
    Technologies: Docker, Deep Learning, TensorFlow, PyTorch, Python
  • Data Scientist

    2017 - 2018
    Market Logic Software
    • Developed and deployed deep learning-based NLU and NLG services for a chatbot and a reinforcement learning-based dialogue manager for some consumer goods and technology world leaders.
    • Developed and deployed a text classification pipeline with active learning.
    • Developed a data analytics prototype for extracting trends and hidden marketing insights from market research documents.
    • Researched question answering, NLG, and abstractive summarization and studied the feasibility and performance of cutting-edge models in production.
    • Researched and studied the feasibility of different methods for leveraging huge incoming client inputs to train the existing models in real time.
    Technologies: Reinforcement Learning, Deep Learning, PyTorch, Python
  • NLP Engineer

    2011 - 2012
    UNDL Foundation
    • Researched and developed statistical models for extraction and alignment of idiomatic expressions across nine European languages.
    • Employed high-performance computing to handle big data.
    • Carried out research and published scientific articles.
    Technologies: MATLAB, Java


  • SNLP: Open Source Python Package for Statistical NLP Methods

    SNLP is a practical package with statistical tools for natural language processing. SNLP is based on statistical and distributional attributes of natural language, and hence most of its functionalities are unsupervised.

  • NCC-Extractor

    NCC-Extractor extracts non-compositional noun-noun compounds from a corpus of text. This model can be used in tasks such as profanity detection, terminology extraction, sentiment analysis, and disambiguation of propositional phrase attachment.

  • Substitution Driven Measures of Association (SDMAs)

    Substitution Driven Measures of Association (SDMAs) can be used to extract collocations or multi-word expressions that have numerous benefits for a wide range of NLP tasks including but not limited to language modeling, topic models, sentiment analysis, document classification, and named entity recognition.


  • Languages

    Python, Java, C++, Python 3, SQL
  • Paradigms

    Data Science
  • Other

    Deep Learning, Multivariate Statistical Modeling, Distributional Semantics, Natural Language Understanding (NLU), Natural Language Processing (NLP), Semantic Composition, Word2Vec, Machine Learning, Artificial Intelligence (AI), Statistics, Statistical Modeling, Data Visualization, Data Analysis, Analytics, Reinforcement Learning, Regression, Clustering, Mixture Models, OOP Designs, Cloud Computing, Software Engineering, Predictive Analytics, Planning, OCR, Software Architecture, Machine Learning Operations (MLOps), Cryptocurrency APIs, Project Leadership, Leadership
  • Libraries/APIs

    PyTorch, NLTK, NumPy, Scikit-learn, Pandas, SpaCy, TensorFlow
  • Tools

    GitHub, Shell, MATLAB, MongoDB Shell, AWS CLI, Seaborn, PyCharm, IntelliJ, Jenkins, ABBYY
  • Platforms

    Linux, MacOS, Amazon EC2, Docker, Google Cloud Platform (GCP), OS X, Visual Studio Code
  • Storage

    MongoDB, Amazon S3 (AWS S3)


  • PhD Degree in Computer Science
    2012 - 2017
    University of Geneva - Geneva, Switerland
  • Master's Degree in Computer Science
    2008 - 2010
    University of Lugano - Lugano, Switzerland

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