Machine Learning/AI Lead

•Tensorflow, Python, R, Various AI/ML algorithms such as Linear regression, Forecast, Time-Series, CNN/RNN/ DNN, XGBoost etc.
•Knows about hyper parameters and how to tune them in various scenarios
•One of our key criteria for selection is how many models the candidate has worked upon, how many of them went into production, quality of data sets and how did the candidate prepare the data.    
•Define enterprise AI and ML technology direction, including tech standards
•Define reference architectures for AI solutions
•Develop Proof of Concept or Proof of Technology applications using AI technologies
•Lead technology evaluations and selections in the AI and ML domains
•Maintain a high competency in current health IT industry and technologies 
10+ years of experience in technology solution design and development 
•  5+ years of experience in AI and Machine Learning technology evaluation, introduction, development and deployment
•Expert knowledge of deep learning algorithms as well as major ML and chatbot libraries
•Deep knowledge of the data science and data engineering aspects of an ML deployment
•Deep knowledge of AI deployment design and platforms (incl. GPUs, containers, cloud, micro services, data centers)
•Excellent writing and oral communication skills
•Proven track record in building and maintaining relationships
•Strong inter-personnel, collaboration and teamwork skills
•Experience with application and data integration, including APIs
•Knowledge of AI project delivery tools and processes, including agile and ModelOps
•Experience in a large healthcare organization
•Extensive experience of working with care delivery business and knowledge of this business domain, including HL7, EMR, biomed devices, clinical imaging, lab, digital health, HIE, etc.
•Broad knowledge of architecture domains, including business, application, data, infrastructure and security architectures    PTC    The position involves deep experience and knowledge of AI technologies, AI solution development and deployment, data science, data engineering, and an ability to collaborate with internal and external stakeholders. The ability to define and communicate technology direction and solution approach is critical. Broad exposure to cutting edge technologies is essential.