Descripción
The Azure Transformation Release Manager is responsible for ensuring that GM data products – including AI/ML models – are compliant with all GM standards.
This is a technical role related to ensuring/refining CICD, SonarQube, Load/Soak/Spike tests (with all artifacts), Business UAT, and other tests as aligned – have been properly defined, executed, and passed – but the intended use case.
Role requires deep familiarity of PRODUCTION use of Azure, DataBricks and MLFlow. Other skills can be taught if the candidate has relevant experience.
Focused on the creation of value via AI/ML.
Must have a decisive mindset, a growth mindset - and be focused on "realizing" models in production. Must have a track record of working in Production.
Cross Functional Leader
Is responsible for working with relevant Product Manager(s), Security, Data Scientists, MLOps teams, Infrastructure teams, business partners, and measurement teams to ensure a shared vision. Able to take models - no matter the state of maturity - and help navigate all these groups to create consensus.
Deeply Technical regarding the intersection of code and infrastructure
Is responsible for the end-to-end data flow, the end to end timing charts, all latency implications, the overall MLC - including but not limited to distributed computing, data flows, connection management, caching strategies, pipeline quality, security compliance, dependencies.
Passionate and knowledgeable about AI/ML
This role is NOT to produce code to design ML solutions from scratch. This role works with Data Scientists or third parties who are responsible for creating ML models (or PoCs of ML models). Evaluation, GAP analysis, code reviews and refactor support - is the key to this role. In certain cases - the ML Engineer will lean in on certain areas of focus to enable the overall team.
Focused on TCO and maintainability of models - for turn over to Managed Services (or third parties)
This role is about creating highly dependable, cost effective, and "fully empowered" ML solutions. Our success criteria are measured in terms of the L3 incident count over time, the ongoing personnel costs and the cloud costs associated with the ML solution. The idea lead - "saves the company time, money, headaches, and allows for accelerated innovation" by freeing up these resources.
Requirements:
- Bachelor's, Master’s Degree or Ph.D. in Data Science, Analytics, Operations Research, Engineering, Statistics, Economics, Computer Science, Applied Mathematics, or another related field.
- 3 years of hands-on experience running a product AI or ML experience.
Remote: The position can be performed remotely from the US most of the time, but the employee may be asked to come on-site approximately four times per year
Compensation: The expected base compensation for this role is : $166,131 - $200,018 . Actual base compensation within the identified range will vary based on factors relevant to the position.
Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays.
#Remote
Additional Description
Como contratamos
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