Assistant Manager

Job Description

Job Responsibilities 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