Amazon · Jan 21st 2019
Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help the largest global enterprises derive business value through the adoption of Artificial Intelligence (AI)? Eager to learn from many different enterprise’s use cases of AWS ML and DL? Thrilled to be key part of Amazon, who has been investing in Machine Learning for decades, pioneering and shaping the world’s AI technology?
At Amazon Web Services (AWS), we are helping large enterprises build ML and DL models on the AWS Cloud and developing variety of AI solutions. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. Our Professional Services organization works together with our AWS customers to address their business needs using AI.
AWS Professional Services for Strategic Accounts is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML or DL models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.
A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions. Major responsibilities include:
· Understand the customer’s business requirements and guide them to a solution using our AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances . · Assist customers by being able to deliver AI/ML/ DL project from beginning to end, including understanding business requirements, developing architecture, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization. · Use Deep Learning frameworks like MXNet, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our customers build DL models. · Use SparkML and Amazon Machine Learning (AML) to help our customers build ML models. · Use Lex to develop Conversational User Interface (CUI) · Work with our Professional Services Big Data consultants to analyze, extract, normalize, and label relevant data. · Work with our Professional Services DevOps consultants to help our customers operationalize models after they are built. · Assist customers with identifying model drift and retraining models.
· A bachelor’s Degree in (Computer Science) or equivalent experience · Basic experience in predictive modeling, data science and analysis · Experience using Python and/or R · Knowledge of SparkML · Good skills with programming languages, such as Java or C/C++ · Exposure to Dev Ops practices and practical Linux and Windows-based systems administration skills in a Cloud or Virtualized environment. · Strong scripting skills, i.e., Powershell, Python, Bash, Ruby, Perl, etc. · Experience using ML libraries, such as scikit-learn, caret, mlr, mllib · Experience handling terabyte size datasets · Track record of diving into data to discover hidden patterns · Familiarity with using data visualization tools · Knowledge and experience of writing and tuning SQL · Experience giving data presentations · Extended travel to customer locations may be required to deliver professional services, as needed · Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our customer’s organization
· Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations · Consulting experience and track record of helping customers with their AI needs · Experience with AWS technologies like Redshift, S3, EC2, Data Pipeline, & EMR Experience with revision control source code repositories (Git, SVN, Mercurial, Perforce). · Experience with management of continuous integration servers like Jenkins, Bamboo and TeamCity. · Experience with automated testing tools (ie. Selenium, JMeter). · Understanding and experience with code deployment (tagging). · Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment