Primary Responsibilities:

• Develop and deploy innovative AI solutions to various business problems. You will have the opportunity to learn and apply some of the most cutting-edge AI technologies

• Research and evaluate new data sources, AI technology trends and methodologies to enhance Organization level data science capabilities and solutions

• Perform data analysis, pre-processing and feature engineering required to ensure quality and reliability of the AI models and optimize them for performance and robustness

• Develop and deploy big data pipelines and frameworks for data ingestion, processing and analysis

• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• Bachelor’s degree or higher in computer science or engineering with a focus on language processing. Overall 7+ years of experience

• Experience in ML R&D with demonstrable impact is preferred. At least 4+ years of experience in NLP and relevant projects. This requirement may be relaxed for candidates with a PhD

• Experience implementing multiple machine learning techniques such as supervised, unsupervised, reinforcement learning, Deep learning, NLP.

• Possess solid technical writing skills and must have published research papers in leading academic journals on topics related to Artificial Intelligence & Machine Learning

• Proven excellent analytical and problem-solving skills, including the ability to disaggregate issues, identify root causes and recommend solutions

• A sound understanding of common NLP tasks such as text classification, entity recognition, entity extraction, and question answering

• Understanding of NLP techniques for language modeling, text representation, and semantic extraction

• Proven good grasp of current DNN architectures, training, and evaluation methods in NLP, and the ability to adapt them for clinical/medical data and problems

• Ability to develop DNN models from scratch using PyTorch/Tensorflow

• Proven good knowledge of and hands-on skills with repository management and GPU use, and the ability to rapidly set up NLP pipelines for testing new ideas

• Familiarity with NLP literature, thrust areas, conference venues, and code repositories

• Proficiency in Python, R, SQL and other programming languages for data analysis and modeling

• Proficiency in libraries such as Huggingface, Spacy, and OpenAI API

• Proficiency with cloud development environments such as Azure, AWS, and GCP See Less