NLP Technical Architect
We are looking for NLP Technical Architect to design and develop efficient self-learning NLP products.
- 10+ years’ experience with proven 7+ years’ relevant experience as an NLP Developer and Architect.
- Work Experience in creating NLP pipelines for processing large amount of document corpus, web crawlers/ scrapers, NLP based chatbots (like Rasa)
- Experience with machine learning libraries (like spacy, scikit-learn), frameworks (like Keras or PyTorch), statistics and classification algorithms.
- Understanding of NLP techniques for text representation, semantic extraction techniques, data structures and modelling.
- Working experience in NLP application areas - Sematic Web and Ontologies, Document Classification, Question-Answer Matching, Text Summarization, Machine Translation, Sentiment Analysis. Have worked with RNN, LSTM etc
- Working knowledge of relational and NO-SQL database systems.
- Knowledge of Python, Java and R. Any other experience with programming languages will be appreciated (but must NOT be the primary skill)
- Working knowledge of deploying and using Machine Learning environments on Cloud (Azure, AWS) and DevOps (CI/ CD) experience
- Ability to write robust and testable code
- Strong communication skills
- An analytical mind with problem-solving abilities
As well as possessing the required technical skills, you will be a confident individual capable of working in a busy development environment with a focus on delivering efficient self-learning NLP products.
- Effectively understand the problem and then design most efficient NLP software architecture
- Study and transform data science prototypes
- Select appropriate annotated datasets for Supervised Learning methods
- Use effective text representations to transform natural language into useful features
- Find and implement the right algorithms and tools for NLP tasks
- Develop NLP systems according to requirements
- Train the developed model and run evaluation experiments
- Perform statistical analysis of results and refine models
- Extend ML libraries and frameworks to apply in NLP tasks
- Remain updated in the rapidly changing field of machine learning
- Working closely with IT Operations to ensure high standards of delivery.