GEP is a diverse, creative team of people passionate about procurement. We invest ourselves entirely in our client’s success, creating strong collaborative relationships that deliver extraordinary value year after year. Our clients include market global leaders with far-flung international operations, Fortune 500 and Global 2000 enterprises, leading government and public institutions. 

We deliver practical, effective services and software that enable procurement leaders to maximise their impact on business operations, strategy and financial performance. That’s just some of the things that we do in our quest to build a beautiful company, enjoy the journey and make a difference. GEP is a place where individuality is prized, and talent respected. We’re focused on what is real and effective. GEP is where good ideas and great people are recognized, results matter, and ability and hard work drive achievements. We’re a learning organization, actively looking for people to help shape, grow and continually improve us.

Are you one of us?

GEP is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, ethnicity, color, national origin, religion, sex, disability status, or any other characteristics protected by law. We are committed to hiring and valuing a global diverse work team.

For more information please visit us on GEP.com or check us out on LinkedIn.com.

• Design and implement ML workflows for seamless integration and monitoring of the ML models. 
• Implement automation for model testing, validation, and deployment. 
• Produce resource utilization and cost optimization strategies. 
• Monitor and optimize ML models in production.  
• Bring in best practices to optimize MLOPs efficiency. 
• Work closely with Data Scientists, Software Engineers, and other stakeholders by bridging knowledge gaps and enable smooth deployment & monitoring of models. 

• 8+ years of experience in Machine Learning Projects with 2 years in managing MLOPs workflows. 
• Knowledge of ML lifecycle and strong exposure to model deployment and monitoring and monitoring metrics. 
• Clear conceptual understanding of basic MLOPs approaches. 
• Previous experience in integrating MLOPs stack with DevOps stack for seamless monitoring.   
• Setting up Model, Data versioning tech stack for trace back. 
• Exposure to some of the ML Ops tools and frameworks Kubeflow, MLflow, DVC, Seldon, Azure MLOPs, Data Bricks.