MLOps Engineer

About the Company OpenKyber is partnering with an innovative organization that leverages advanced analytics, machine learning, and data driven decision making to optimize business performance at scale. The company is committed to delivering intelligent solutions that improve customer experiences, drive revenue growth, and support strategic pricing initiatives. With a collaborative culture and strong investment in technology, this organization empowers talented professionals to make a measurable impact through data and engineering excellence. Role Summary:

We are seeking an experienced MLOps Engineer to serve as the engineering foundation of a growing Pricing Data Science team. In this role, you will transform machine learning models developed by Data Scientists into scalable, reliable, and production ready solutions that directly influence pricing decisions across the business. You will own the infrastructure, automation, deployment, and monitoring processes that bring machine learning models into production. Working closely with Data Scientists, you will build robust pipelines, APIs, and workflows that ensure pricing models operate efficiently, securely, and at scale. Your work will be critical to enabling data driven pricing strategies and delivering meaningful business outcomes.

• Design, build, and maintain scalable MLOps pipelines that support machine learning deployment and monitoring
• Develop and manage CI/CD pipelines to automate model and application deployments across environments
• Build and optimize data and machine learning workflows using Python, SQL, Airflow, Composer, Git, and cloud technologies
• Deploy, manage, and support machine learning models using Vertex AI or comparable platforms
• Implement model monitoring, logging, alerting, performance tracking, and automated retraining processes
• Develop APIs and services that integrate pricing models with business applications and downstream systems
• Collaborate closely with Data Scientists to productionize machine learning models and operationalize solutions
• Troubleshoot production issues while continuously improving performance, reliability, security, and maintainability

• 5+ years of experience in software engineering, MLOps, machine learning infrastructure, or a related engineering discipline
• Strong hands on Python development skills with the ability to write, test, and maintain production quality code
• Advanced SQL experience working with large scale transactional, pricing, or analytical datasets
• Demonstrated experience building and supporting production machine learning pipelines and deployment frameworks
• Experience developing CI/CD pipelines and working with Git and GitHub in enterprise environments
• Strong understanding of the machine learning lifecycle, including deployment, monitoring, automation, and retraining
• Experience building REST APIs, microservices, and containerized applications using Docker
• Excellent collaboration, communication, problem solving, and stakeholder partnership skills, particularly when working alongside Data Science teams

• Experience with Google Cloud Platform, including Vertex AI, BigQuery, Cloud Storage, and Composer
• Experience implementing scalable monitoring and observability solutions for machine learning environments
• Familiarity with pricing analytics, recommendation engines, or customer targeting models

Why Join This Opportunity This is an opportunity to play a highly visible role in a growing Pricing Data Science function where your work will directly impact revenue driving decisions. You will partner with talented Data Scientists, work with modern cloud and machine learning technologies, and help shape the framework that brings advanced analytics into real world business applications.

Apply Today If you are a hands on MLOps Engineer who enjoys building production grade machine learning systems and partnering with Data Scientists to deliver scalable business solutions, we want to hear from you. Apply today through OpenKyber and submit your resume along with your contact information for immediate consideration.

For applications and inquiries, contact:View email address on us.fitly.work