Assistant Manager

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