Lead MLOps Engineer

hubQuest is a team of experts in IT and analytics focused on providing innovative solutions by forming and expanding tech teams to help partners become data-driven organizations. The role supports a partner's Global Analytics unit, a centralized team dedicated to strengthening data-driven decision-making and creating smart data products for daily operations. The Global Analytics team includes Data Scientists, Data Engineers, ML Engineers, MLOps Engineers, Business Intelligence Specialists, Software Developers, and UX Designers across three continents and five countries. They develop AI-powered sales and analytics ecosystems with capabilities such as consumer behavior alerts, route optimization, product recommendation engines, forecasting, and intelligent sales insights.

• Advanced degree in Computer Science, Engineering, Mathematics, or related STEM field
• 5+ years of experience in MLOps, ML Engineering, Platform Engineering, or software engineering supporting ML systems in production
• Strong understanding of machine learning concepts and experience operationalizing ML solutions at scale
• Strong Python software engineering background
• Experience designing, deploying, and scaling complex AI-driven applications in production environments
• Experience with ML frameworks such as TensorFlow or PyTorch and MLOps tools such as MLflow
• Strong understanding of CI/CD principles and MLOps practices
• Experience designing scalable ML deployment and orchestration architectures
• Expertise in DevOps technologies including Docker and Kubernetes
• Strong Azure experience and familiarity with services such as Azure Machine Learning, Databricks, Azure Data Factory, or equivalent cloud tools
• Experience working with large-scale distributed data environments (e.g., PySpark)
• Strong understanding of software architecture and engineering design patterns
• Experience with Git and Agile environments
• Ability to communicate effectively with both technical and business stakeholders
• Professional and service-oriented mindset
• Fluent English

• Own technical direction and operational excellence of complex AI-driven business solutions deployed globally
• Define architecture and engineering standards for scalable production-grade AI applications
• Shape engineering and MLOps best practices across a multi-module analytics product ecosystem
• Lead and coordinate technical teams toward sprint goals and delivery excellence
• Partner with Product Owner to translate business priorities into technical roadmaps and engineering initiatives
• Guide teams in architectural decisions across interconnected AI, data, and application components
• Drive technical decision-making and establish engineering standards across ML and software teams
• Review technical solutions and pull requests to ensure maintainability, scalability, and operational reliability
• Support teams in improving engineering practices and delivery effectiveness
• Design cloud-native architectures supporting AI-powered business products
• Ensure AI solutions are observable, scalable, maintainable, and production-ready
• Own deployment standards, monitoring approaches, lifecycle management, and reliability practices for AI-driven applications
• Collaborate closely with Data Scientists, Data Engineers, and Software Engineers to operationalize intelligent business solutions
• Support engineering teams in integrating AI services, APIs, pipelines, and business-facing application components
• Drive standardization and continuous improvements across the AI product landscape