ML Engineer (NLP)

Responsbilities

1. Language Modeling
   • Ability to assess and implement business use-cases through NLP
   • Keen sense of business value
   • Strong sense of where NLP does and does not apply well
   • Fluent in python programming with strong discipline in best practices
   • Experience in the full software / data science life cycle
   • Experience in production deployment of NLP models
   • Experience in some of:
     • ML Frameworks: Pytorch, Keras, Tensorflow
     • NLP libraries (allen NLP, spaCy, NLTK, etc)
     • Transformer models
2. Data Pipeline Engineering
   • Applied knowledge in feature engineering and data preparation
   • Applied knowledge in data mining and text mining
   • Solid understanding of end-to-end workflows involving NLP
   • Experience in moderate to complex NLP solutions and use-cases
3. Project Delivery
   • Technical leadership and/or engineering roles through the entire project lifecycle
   • Ability to collaborate with various team members
   • Able to execute quickly with reasonable estimation and time-boxing
   • Applied knowledge in properly defining experiments and evaluating outcomes

Ideal candidate profile:

• 3+ years experience in a technical consulting firm
• 4+ years of direct application in language modeling and NLP engineering
• Working knowledge across diverse NLP modeling scenarios
• Experience in scoping, estimating, and delivering NLP solutions
• Experience working in one or more cloud providers: AWS, IBM, Microsoft
• Exposure to delivering scalable NLP solutions
• Established proficiency across NLP methodologies and techniques, including:
  • Entity recognition, entity linking, and entity resolution
  • Topic modeling and text similarity
  • Document classification or clustering
  • Word, sentence, and document embeddings
  • Semantic search or information retrieval