Ideal Candidate
The ideal candidate will have expertise in NLP, deep learning (Keras, TensorFlow), distributed data
engineering, cloud development (GCP) and proficiency with Python. This person will be tasked to
architect and build language models (extraction, summarization, classification, understanding,
generation, sentiment analysis) to make better sense of massive sets of unstructured data.

Requirements
•M.S in Computer Science, Statistics, Machine learning, Applied Math, PhD desirable
•5+ years of experience in engineering supervised and unsupervised learning systems
•Proven experience in cloud development (GCP) and machine learning development life cycle
•Expertise building deep learning NLP models (Embeddings, Transformers, Transfer Learning)
•Experience in data quality: scraping, munging, tagging/annotation, lexicons, curating training data
•Experience with distributed data pipelines (Spark, Google Cloud Data Flow)
•Worked with data Relational and NoSQL database repositories
•Strong hands-on programming experience in Python, Java
•Expertise in deep learning frameworks for NLP tasks (TensorFlow 2.0, Keras, NLP packages)
•Experience in REST based web service development
•Experienced in agile development and tools (Jenkins -CI/CD, Jira, Docker, Kubernetes)
•Experience with explainability techniques of AI modes is a huge plus
•Ability to learn new technologies and rapidly prototype newer concepts
•Top-down thinker, excellent communicator, and great problem solver