What are the responsibilities and job description for the Principal AI Architect (BHJOB22048_622) position at ITmPowered?
Principal AI Enterprise Architect – ITmPowered
Principal AI Architect is responsible for defining and executing technology and adoption strategies as well as target architectures for AI and related topics. Spearhead and drive enterprise transformation across AI / ML / DL technologies, AI solution development and deployment, Machine Learning, Deep Learning, data management. Set vision, strategy, and direction for Artificial Intelligence, Machine Learning, and Deep Learning programs, frameworks, platforms, and initiatives. Engage, collaborate, and influence Senior Business Executives, IT leadership, and external stakeholders.
Responsibilities :
Lead and define enterprise AI technology and adoption strategy
Lead the definition of reference architectures and related technology standards for AI and Machine Learning
Lead the development of appropriate Proof of Concept or Proof of Technology efforts
Provide strategic consulting to business and IT executives for AI introductions and adoption
Make AI-related architecture and technology recommendations to senior business and IT executives
Provide strategic consulting to architects and AI implementation initiatives for AI solutioning and deployment
Lead technology evaluations and selections in the AI and ML domains
Participate in architecture review and approval processes for program architectures
Maintain a high competency in current health IT industry and technologies
Requirements – Your qualifications
Master’s Degree in statistics, mathematics, computer science, or related field of study required.
D. in statistics, mathematics, computer science, physics or related field of study preferred.
15 years of experience in technology strategy consulting and architecture leadership.
Proven track record of technology transformation, business engagement, and sr executive influencing.
Extensive AI and Machine Learning technology evaluation, introduction, development and deployment.
5 years of machine learning techniques and algorithms such as k-NN, Naïve Bayes,SVM, Decision Forests, Boosting, Ensembling, Neural Networks, etc.
Expert knowledge of deep learning algorithms as well as major ML and chatbot libraries
Deep understanding of theory and practical application of machine learning methods, to include neural net, support vector machines, RRTs, MDPs, Bayesian or ML algorithms and numerical methods.
Experience with common data science / ML toolkits such as NumPy, SciPy, Scikit-Learn, Tensor Flow, Torch, Keras, Caffe, MXNet, etc
6 years of experience using quantitative methods : Modeling, machine learning, feature creation, construct analysis, multivariate statistical analysis and model building, and predictive modeling.
Expertise in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, anomaly detection, sequential pattern discovery and text mining.
Ideal candidate will be skilled in natural language processing, predictive and classification algos.
4 years of experience writing SQL code in relational database environment.
5 years of experience writing statistically-related code in ‘R’, Python, or Matlab (or equivalent), SAS. with focus on clarity, reproducibility and reusability
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