Position: ML OPS Consultant
Company: GlobalLogic
Category: Data Science
Experience required: 5y
English level: intermediate

We are looking for an ML OPS (Associate / Consultant level) to join our ambitious project dedicated to one of the Clients of our Fin-tech portfolio.

MLOps are creating a new intuitive way for Machine learning practitioners and software engineers to discover, build and deploy ML at scale. MLOps are developing a state of the art platform for Data Scientists, Data Engineers, Machine Learning Engineers and Software Engineers to experiment, build and deploy ML models at scale using AWS. You will be working using all the best practices of software engineering into the domain of data science, meaning we aim to incorporate data/model versioning, collaboration, monitoring etc. in an intuitive way that allows our users to prosper within the field.

As an ML Engineer in the MLOps team you will be a key stakeholder and owning a lot of responsibility in developing the backend on AWS to deliver world class practices within data engineering and machine learning modelling.

We utilize AWS and Sagemaker in combination with many other AWS services to deliver a SaaS product world wide to our customers accounts. 

It is a great chance to become a part of a distributed, cross-functional team based in Poland. This Project is a part of the Multi-Year Program Plan.


Requirements:
Have 3+ years experience in working with AWS (DS/ML Stack).
Solid knowledge of PySpark.
ML Ops experience: knowledge of a variety of machine learning techniques + hands on Amazon Sagemaker.
Solid experience working with LINUX software.
Good understanding of SDLC life-cycle (Dev, test, preprod, production and the CI/CD pipeline).
Familiar with tools like Git, Bash, etc. 
Team player skills with ability to work in distributed delivery environment.
Analytical mind and business acumen.
Good English communication skills (written & spoken). 


Job Responsibilities:
You design ML deployment and support systems, including architecture and tool selection. The specific tools can depend on the client’s environment and constraints ( it would be nice for our own sanity if we could standardize to a cloud agnostic stack but that is an ongoing discussion )
You help the client deploy and adopt the MLOps system. Our delivery teams are multi-disciplinary scrum teams so the MLOps Engineer is not expect to build the whole system themselves but they are expected to own the overall architecture and make sure the work to be done is well defined and advancing well
You work with data science teams ( both the Moov AI one and potentially the clients  ) to make sure the solution is using the MLOps system in an efficient and appropriate way.