ModelOps Engineer

We are looking for ModelOps Engineer x 3

Salary is less than PHP 70K per month (basic + Internet allowance PHP 2000 + Rice allowance PHP 1000)
Working Location : work from home in PH
Permanent positions, Candidates from Philippines only
Interested candidates please email your resume to: ----------

About Client : Trax is the driving force of the store of the future. The world’s top consumer goods companies and retailers use our cloud platform to gain the power to see what happens at shelf and the agility to delight shoppers in new ---------- scription - ModelOps Engineer
Serves as the primary, hands-on interface between all groups that implement and benefit from Enterprise AI.
Assists in the integration of ModelOps capability into enterprise IT systems and business applications.
Monitors model performance and ensures rapid response to any issues that arise.


ModelOps requirements:
• Ability to reflect technical and business KPIs in an automated
ModelOps framework
• Ability to enforce technical standards that conform models
from any source to common standards for deployment, monitoring
and governance, without imposing undue restrictions
or requirements on Data Science, DataOps, DevOps, ITOps or
compliance teams.
• Ability to implement processes that fit the unique requirements
for consistent adherence to the SLA’s of the business through
mutually agreed-upon rules, triggers, and/or alerts
• Ability to quickly spot, resolve and report on issues with any
part of the MLC process (technical, business, approval, reporting,
etc.).


Description - ModelOps Engineer

Acts as the technical liaison between business, data science, ITOps, DataOps DevSecOps and compliance teams. The ModelOps Engineer works with the extended cross-functional teams to translate the blueprints and templates provided by the EAIA into a specific MLC for every model under development. This ensures that all models meet relevant standards for efficacy, datausage, interpretability, auditability, fairness and of course, ROI – and does so in a consistent way.
The centralization of ModelOps as a discipline provides maximum flexibility for business units and data science teams to satisfy their unique business requirements without being locked into a single, restrictive approach, while maintaining visibility
and accountability across the enterprise.
Together, the Enterprise AI Architect and the ModelOps Engineer form the core of a function that
ensures that models are operating and conforming to business, technical and compliance KPIs 24x7.