AWS Kubernetes Docker Experince Shell Script Python Engineer (Jobs in Smithfield,   Requirement id 93548 Job title Engineer Job location in Smithfield, RI Skills required AWS, Azure Cloud, Kubernetes, Docker Experince Shell Script Python Open Date 07-Jun-2019 Close Date Job type Contract Duration 4 Months Compensation DOE Status requirement --- Job interview type --- Apply    Email Recruiter: coolsoft Job Description Engineer: AWS, Azure Cloud, Kubernetes, Docker Experince Shell Script Python Candidate must be our W2 Employee Description: Our client is seeking savvy engineer to join our early generation team of analytics platform and Machine learning engineers. As a team member, you will be responsible to support data scientist and other ML engineers through their evolving need to manage AI machine learning model life-cycles in partnership with other cross functional teams. You have clear understanding of AI models built on Python with AI frameworks, expert understanding of REST APIs, and have expertise with Kubernetes, Docker containerization, and deployment to AWS services. You will support our data scientist, model engineers, model DevOps, and data analysts to build an optimal pipeline. You are self-directed and comfortable supporting the ML Ops needs of multiple stakeholders, systems and products. You will also be responsible for aligning them with the proposed architecture. The right candidate will be excited by the prospect to build out analytics platform bottoms up to support our existing and next generation of ML-driven products and solutions initiatives. Required Skills: Experience containerizing and deploying applications and services to cloud platforms such as Amazon Web Services (AWS), Azure, and Google cloud using Kubernetes and Dockers. Current experience managing life-cycle of machine learning models deployed to the cloud using CI/CD tools such as Concourse. Experience with AI model physical architecture and an ability to interact with architects to arrive at solutions will be key. Prior experience supporting data scientist and building models using Python, Tensor flow, H2O.AI etc. is required. Candidates will be required to possess the necessary organizational and leadership skills to work with multiple cross functional teams across the enterprise. Deployment expertise of models on AWS EKS platform at scale Strong understanding of AWS services including EC2, S3 Buckets, EKS Hands on experience with Docker and Kubernetes for the cloud are a must. Knowledge of model data set staging and familiarity integrating with big data sets that meet functional and non-functional business requirements. Proficiency with packaging Python, Python objects, Python modules, Tensor flow, Tensor flow serving, Fastex, H20.ai and other ML enabling tools. Knowledge/proficiency with technologies such as Hadoop, Yarn, Trifacta, EMR, FastCore etc. Qualifications: Experience containerizing and deploying applications and services to cloud platforms such as Amazon Web Services (AWS), Azure, and Google cloud using Kubernetes and Dockers. Current experience managing life-cycle of machine learning models deployed to the cloud using CI/CD tools such as Concourse. Experience with AI model physical architecture and an ability to interact with architects to arrive at solutions will be key. Prior experience supporting data scientist and building models using Python, Tensor flow, H2O.AI etc. is required. Candidates will be required to possess the necessary organizational and leadership skills to work with multiple cross functional teams across the enterprise. Being a highly motivated and responsive team player who can multi-task and work under minimal supervision is a must. Previous experience: Containerize AI ML model artifacts from data scientist into containers that can be deployed into training, challenge, or production deployment pipelines supporting data recipe, model, and performance containers. Deployment expertise of models on AWS EKS platform at scale Strong understanding of AWS services including EC2, S3 Buckets, EKS Hands on experience with Docker and Kubernetes for the cloud are a must. Knowledge of model data set staging and familiarity   Call 502-379-4456 Ext 100 for more details. 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