Machine Learning Architect

Required skills/Experience: Extensive experience in software development and architecture, with a focus on AI and machine learning. Proven experience in designing and implementing large-scale AI solutions. Experience in system integration. 

Technical Skills: Strong understanding of Data Science tools and technologies, hands-on experience (at least 8 Years) in applying appropriate techniques to build advanced Machine-Learning models, including handling of all pre- and post-modeling deliverables. Very strong hands-on working knowledge of Python and SQL. Expert with Machine Learning methods like Boosting algorithms, Decision trees (CART, Random Forest etc.), Support Vector Machines (SVM), Naïve Bayes, KNN, and unsupervised learning techniques like PCA, Clustering. Highly skilled in ML Ops principles, including code versioning (using Git), monitoring, re-training, deployment, CI/CD and ensuring seamless integration of machine learning models into production environments. Should have worked across both structured and unstructured data (text) leveraging classification, regression, unsupervised, NLP and other techniques. Performed assignments that involved providing analyses and insights directly to client stake holders, proposed ideas for building strong internal/external analytics processes; performed analytics opportunity identification, roadmap development, technology, requirements analysis. Experience or familiarity with healthcare industry practices, particularly in payor operations, is highly preferred. Experience in working with Service-oriented Architectures, APIs and in deploying services into containerized orchestration environments such as Kubernetes. Experience in engaging with Platforms/Product-centric Agile Development processes