ML Engineer

Visa: USC/GC only due to legal or gov. contract req Tax Term: W2/C2C/Own Corp Hybrid has to be onsite 2x a week either in Plano, TX or Pleasanton, CA. (for someone who is absolute rockstar could potentially make exception) This role is intermediate level, we are looking for someone with 5 to 10 Years, Project Description: 2 main projects: • Personalization – focused on customer – how do we solve customer related problems. Products/content, putting the right product/content in front of the customer. The team call Customer Team • Business growth – anything that is related to Supply Chain, inventory, price, merchandising In both of these fields/projects, Manager works closely with Data Science team to put the Data Science model into production, which means Aravind’s team, build the pipelines end to end which includes data preparation and data cleaning, training, model evaluation, model metrics. Day to Day Overview: This Engineer will be responsible for sending the model to production which means creating the pipeline which can run the task, training and put it into production. Someone else would have to consume it and it would have to be expose to some serving API layer. MUST Have Skills: • Having ML Engineering background and understanding the ML Lifecycle experience is critical o Candidate needs to understand at what stage what is happening and what needs to be done • Need someone who is strong technically, using spark as a way of using distributed framework and having deep knowledge in that. If using data from snowflake or maybe using data from data lake, the candidate will need to understand how the data is organized how you structure the data in order to have an optimal read. How do you write queries to make sure the performance of the data that you are reading on the read queries is optimal. • The 2nd aspect we look in the candidate, once these models are done it's some other consuming application need to consume it. o Example, customer if you now producing recommendations for the customer it needs to be shown to different end, on an app or mobile application so we are looking at integrations working closely with those teams around building the integration points so that's becomes like a software engineering skill set. • This is NOT a Data Science role, they will not be looking at what model and what algorithm that they need to solve the business part. Not looking for data science who understand the model