FBS – Farmer Business Services is part of Farmers operations with the purpose of building a global approach to identifying, recruiting, hiring, and retaining top talent. By combining international reach with US expertise, we build diverse and high-performing teams that are equipped to thrive in today’s competitive marketplace.
We believe that the foundation of every successful business lies in having the right people with the right skills. That is where we come in—helping Farmers build a winning team that delivers consistent and sustainable results.
Since we don’t have a local legal entity, we’ve partnered with Capgemini, which acts as the Employer of Record. Capgemini is responsible for managing local payroll and benefits.
What to expect on your journey with us:
A solid and innovative company with a strong market presence
A dynamic, diverse, and multicultural work environment
Leaders with deep market knowledge and strategic vision
Continuous learning and development
The new ML Ops team will be our centralized shared services team supporting all ML Ops capabilities such as training, deployment, monitoring and feature stores. They will be responsible for the strategy and implementation of these capabilities as well as best practices for the business units to follow.
The Sr. ML Ops Engineer will support the ML Ops team and will work to build out the strategic ML Ops capabilities along with other engineers on the team. They will need to be proactive, own user stories, and follow engineering best practices from the team engineering
We count on you for:
Delivering specific ML Ops engineering tasks such as moderate to complex level designing, developing, implementing, optimizing, and maintaining models, systems, and applications using existing and emerging technology platforms.
Collaborating with cross-functional architecture teams to define and integrate frameworks and roadmaps for machine learning solutions, projects are generally of moderate complexity.
Consulting on the design, development, and implementation of DevOps and ML Ops pipelines. May lead portions of deployment processes under guidance from people leader. Reviews, verifies, validates, and troubleshoots code to ensure high availability and high performance of machine learning models and applications.
Using complex knowledge and understanding of code management principles and best practices to follow architectural and governance guidelines.
Effectively communicating and applying machine learning engineering value, concepts, and strategies across multiple scenarios.
3-6 years of experience in a similar role
Bachelor's degree in management information systems, computer science or similar
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