Principal Engineer

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Responsible for Artificial Intelligence/Machine Learning model development, feature engineering, model validation, deployment and observability in real time and batch setup; lead the study and transformation of data science prototypes; research, design, and implement ML algorithms and tools; lead the consolidation and implementation of new concepts and processes in areas including information retrieval, distributed computing, large-scale system design, networking, data storage, security, AI, natural language processing, UI design, and mobile; serve as a subject matter expert on latest industry knowledge to improve the systems/processes related to ML, Deep Learning, Responsible AI, Gen AI, Natural Language Processing, Computer Vision and other AI practices; set the standards for datasets and data representation methods; extend existing ML libraries and frameworks; determine processes and standards for running machine learning tests and experiments; design,
develop, test, deploy, maintain, and improve machine learning system software; architect, design and implement the Big Data applications on GCP Cloud; support the data migration activities from On-premise Big Data Hadoop
systems to GCP Cloud; analyze the data and workloads of applications and design the cloud appropriate GCP services with optimal usage of cloud resources; collaborate with Data Engineers/Scientists to understand the
application requirements and design/implement the cloud platform according to the ML application needs with optimal cloud resources that operates within the performance and cost requirements; set the strategy for ML/AI
tools/processes, determine the future business needs, partner with stakeholders across business units to define an overall plan for the Registry, model governance and inventory tracking.

Bachelor's or foreign equivalent degree in Computer Science, Applied Computer Science, Information Technology, or a related field, and 5 years of progressive, post-baccalaureate experience in the job offered or as a data scientist, data analyst, AI/ML engineer, or in a related/similar position. Experience therein to include 5 years in end-to-end data science life cycle, feature engineering, model building and model deployment; 3 years in data engineering, data analysis, designing and developing batch processing and streaming applications, using big data technologies, Hadoop, Spark, Hive, Python, R, SQL, Machine Learning, and data science algorithms; 3 years in analyzing and building predictive and Generative AI models and deployment in real time and batch, including RAG (Retrieval-Augmented Generation), Infobots, Search, and LLM (Large Language Models); 2 years with On-prem and cloud based infrastructure, MLE DevOps implementation, MLE tools and
reporting tools like Tableau, Power BI and Looker. Hybrid role, ability to work from home.

Must Reference: Job # SHSEES-W.

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