Machine Learning Engineer

Amazon (Buenos Aires, Argentina) Publicado hace 23 horas
Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) solutions? 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 models on the AWS Cloud. 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 ML.
AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the ML enablement of our customers. If you have experience with ML, including building, deploying, and monitoring models, we’d like you to join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.
This role will focus specifically on AWS’ most complex and largest customers in the world to help solve a wide range of business problems. Consultants will provide deep and broad insight to customers and partners to help remove constraints that prevent them from leveraging AWS services to create strategic value.
A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions. It will be a person who loves to learn, and wants to innovate in the world of ML.
Major responsibilities include:
· Understand the customer’s business need and guide them to a solution using Amazon SageMaker, AWS AI Services, and Amazon EC2 GPU Instances .
· Assist customers by being able to deliver a ML project from conception to production, including understanding the business need, transforming and exploring data, building & validating ML models, and deploying completed models to deliver business impact to the organization.
· Assist customers by developing MLOps (Machine Learning Operations) workflows for data preparation, deployment, monitoring and retraining
· Use Deep Learning frameworks like PyTorch, Tensorflow, and MXNet to help our customers build DL models.
· 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.
· Improving ML models reproducibility, auditing, and performance capabilities
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