Lead ML Engineer

Working with Sagemaker, Tensorflow, Pytorch, Triton, Spark, or equivalent large-scale distributed Machine Learning technologies on a modern containerized deployment stack using Kubernetes, Spinnaker, and other technologies
Experience building Distributed microservices on AWS, GCP or other public cloud substrates
Eat, sleep, and breathe services. You have experience balancing live-site management, feature delivery, and retirement of technical debt
Partner with Product Managers, Architects and Data Scientists to understand customer requirements, and help translate requirements to working software
Own the technology for fully orchestrated machine learning APIs for Einstein Platform
Contribute to the long-range plan, and help drive the microservices architectures for machine learning
Designing, developing, debugging, and operating resilient distributed systems that run across thousands of compute nodes in multiple datacenters
Participate in the team s on- call rotation to address complex problems in real-time and keep services operational and highly available
Create and enforce processes that ensure quality of work, and drive engineering
excellence
Exhibit a customer-first mentality while making decisions, and be responsible and accountable for the output of the team
Partner with vendors like AWS and Data Science teams to pick best fit in terms of libraries and compute to deliver cost effective and scalable model hosting and
tuning/training capabilities
Work collaboratively in a geographically distributed teams in North America, EMEA and APAC

Core Qualifications :

BS, MS, or PhD in computer science or a related field, or equivalent work experience with 9 to 15 years of experience
3+ years of hands-on experience with designing and developing complex big data, machine learning systems, and microservices architectures
Track record of leading highly impactful projects from conception to production