PriceSenz Hiring Event!
Date: Tuesday, September 29, 2020
What We're Hiring For
MLOps Engineer - DevOps & AWS - Remote
Enterprise Solution Architect - Remote
Must have 4 years of MLOps Experience
Must have 7 years of Solutions Architect Experience
The interview will take place on Indeed's virtual interviewing platform. After RSVP, you will find a link in your email that will lead you directly to the video interview lobby during the event. Try to find a quiet place with good lighting and a stable internet connection.
What to bring to this event
What to wear
Dress code is Business casual (dress pants/skirt, button down/blouse, optional tie).
We believe that the ability to deliver more human-like and intuitive experiences, at every touch-point, for every customer, is the single most strategic investment for the modern enterprise. Enterprises must change their business and operating models to deliver such customer experience. The term ‘digital transformation’ is everywhere these days, and every business is experiencing it at some level. But 70% of the digital transformation initiatives are doomed to fail. Traditional approaches to technology delivery just don’t apply anymore and the shift is essential, but where to start? What specifically does your organization need to do to move forward? PriceSenz - led by a few recognized digital practitioners in the industry - can guide you through the digital transformation of your enterprise. You may be at nascent stages or advanced stages in your digital journey, but regardless we can be your trusted partner in realizing your transformation objectives.
What is a Virtual Hiring Event?
Virtual hiring events are a great way for employers and jobseekers to connect, even if they aren't in the same physical location. Hiring is a human process, and they would like to talk with you online (either through chat, on the phone, or video) to see if you’re a fit!
MLOps Engineers with 4-6 years of experience. Deploy ML at scale by creating DevOps pipelines that automate the data collection, prep, transform, analyze, experiment, train, validate, serve, monitor, etc stages.
Skills required :
Strong DevOps, Data Engineering and ML background w/ AWS
Experience with one or more of MLOps tools: ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC)
Experience in Distributed computing, Data pipelines, and AI/ML.
Experience setting up and optimizing DBs for production usage for ML app context
Experience in Docker, Kubernetes, Jenkins.
Experience in Spark, Kafka, HDFS, Cassandra
Strong Python, Scripting Experience, Jupyter notebooks.