hubQuest
hubQuest is seeking a senior MLOps Platform Technical Owner to lead the scaling of global analytics capabilities through production-grade AI and data systems. This is a deeply technical ownership role — not a daily feature-coding role, not a Product Manager role, and not a people management position — focused on architecture, standards, and operational excellence to ensure business-critical AI products are deployed, monitored, and scaled reliably across international markets.
About the Role
You will define and evolve MLOps standards, deployment patterns, model lifecycle practices, monitoring and observability expectations, reliability principles, and production-readiness criteria for ML and AI capabilities used across complex business products. You will work closely with Product Owners, business stakeholders, and technical teams. You must be able to read and challenge technical designs, understand architecture-level trade-offs, review implementation approaches, and guide engineers based on hands-on experience.
What You Will Do
Own MLOps direction for AI and analytics products across global markets.
Define and evolve MLOps standards, deployment patterns, model lifecycle practices, monitoring, and observability expectations.
Challenge proposed solutions, assess production readiness, and explain trade-offs in model serving, CI/CD, monitoring, retraining, integration, scalability, and support.
Guide technical teams, influence decisions, facilitate alignment, mentor engineers, and support technical planning.
Connect business expectations with engineering reality to ensure product evolution is guided by strong MLOps judgment.
Work closely with Product Owners, business stakeholders, and technical teams to align on product evolution and operational priorities.
Review implementation approaches and guide engineers based on hands-on experience.
Support technical discovery, risk analysis, dependency clarification, and strategic planning.
What We Are Looking For
Senior MLOps practitioner with experience building, deploying, monitoring, and operating production ML/AI systems.
Proven ability to define and evolve MLOps standards, deployment patterns, model lifecycle practices, monitoring and observability expectations, reliability principles, and production-readiness criteria.
Ability to challenge proposed solutions, assess production readiness, and explain trade-offs in model serving, CI/CD, monitoring, retraining, integration, scalability, and support.
Experience reading and challenging technical designs, understanding architecture-level trade-offs, reviewing implementation approaches, and guiding engineers based on hands-on experience.
Proven credibility from building and operating production-grade ML, AI, data, or software systems.
Strong communication skills to explain technical concepts to Product Owners, business stakeholders, and technical teams.
Experience working with cross-functional teams and influencing technical decisions.
Experience with AI-powered capabilities such as forecasting, optimization, recommendation engines, intelligent alerts, route optimization, business insights, and advanced data products.
If you have built and operated production ML/AI systems, understand MLOps in practice, and want to create impact through technical ownership and product-oriented MLOps direction rather than daily feature coding, we would love to hear from you.