Design, develop and implement solutions for a wide range of NLP use cases involving classification, extraction and search on unstructured text data
Create and maintain state of the art scalable NLP solutions in Python / Java / Scala for multiple business problems. This involves :
Choosing most appropriate NLP technique(s) based on business needs and available data
Performing data exploration and innovative feature engineering
Training and tuning a variety of NLP models / solutions which include regular expressions, traditional NLP models as well as SOTA transformer based models
Augmenting models by integrating domain specific ontologies and / or external databases
Reporting and Monitoring the solution outcome
Work experience with document-oriented databases such as MongoDB
Collaborate with ML engineering team to deploy NLP solutions in production - both on premise as well as cloud deployment
Interact with clients and internal business teams to perform solution feasibility as well as design and develop solutions
Open to working across different domains – Insurance, Healthcare and Financial Services etc.
Required Skills :
Experience (including graduate school) on training machine learning models, applying and developing text mining and NLP techniques
Exposure to OCR and computer vision
Experience in extracting content from documents is preferred
Experience (including graduate school) with Natural Language Processing techniques is required
Hands on experience with Natural Language Processing tools such as Stanford CORE-NLP, NLTK, spaCy, Gensim, Textblob etc.
Experience / Familiarity with document clustering in supervised un un-supervised scenarios
Expertise in at least two of the state of the art techniques in NLP like BERT, GPT, XL Net etc.
Applied experience of machine learning algorithms using Python
Organized, self-motivated, disciplined and detail oriented
Production level coding experience in Python is required
Ability to read recent ML research papers and adapt those models to solve real-world problems
Experience with any deep learning framework, including Tensorflow, Caffe, MxNet, Torch, Theano
Experience with optimization on GPUs (a plus)
Hands on experience with using cloud technologies on AWS / Microsoft Azure is preferred