Data Collection and Curation.
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 ML/DL/NLP models 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.
Building recommendation engines to increase the personalization of content for the user.
Testing and deployment of models on cloud services i.e. AWS/GCP etc.
Act as a bridge between product and engineering team to translate product requirements into the technical solutions.
Stay updated and use industry best practices and latest technology trends.
Prototype ideas rapidly using cutting edge technologies, drive architecture discussions, and proposes solutions to system and product changes.
Deep understanding of NLP techniques for text representation, semantic extraction techniques, data structures and language modelling.
Experience with NLP libraries and technologies including Spacy, NLTK, Gensim, Stanford NLP etc.
Proficiency with Python and basic libraries for machine learning such as Numpy, Scikit-learn and pandas.
Exposure to DL Model optimisation and transfer learning technique.
Experience of cloud environment AWS/GCP, Hadoop/Spark, SQL is a plus.
Experience with technologies like Docker, Kubernetes, CI/CD, Git is a plus.
Experience with ML Ops is a plus.
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