MLOps Platform Technical Owner

hubQuest builds and scales advanced data and analytics hubs for global organizations, enabling scalable analytics, advanced AI use cases, and data-driven decision-making across regions. They support a partner's Global Analytics unit, an international team building smart data and AI-powered products for daily business operations.

• 5+ years hands-on experience in MLOps, ML Engineering, Platform Engineering, Software Engineering, Solution Architecture, or production AI systems
• Proven experience building, deploying, operating, monitoring, or scaling production-grade ML, AI, data, or software solutions
• Strong understanding of ML concepts and operationalization of ML/AI in production
• Python-based software engineering experience
• Experience with Docker, Kubernetes, CI/CD, DevOps, infrastructure automation, and cloud-native deployment
• Experience with large-scale data environments such as Databricks, PySpark, distributed processing, data pipelines, or equivalent
• Understanding of software architecture, system integration, APIs, design patterns, and production-grade delivery
• Agile experience and strong stakeholder management and communication skills
• Fluent in English

Nice to have:

• Experience as MLOps Technical Lead, MLOps Product Owner, MLOps Platform Lead, ML Platform Lead, Solution Architect, Technical Owner, or similar roles
• Experience overseeing MLOps capabilities, ML platforms, AI solutions, analytics or data products used by business stakeholders
• Experience improving reliability, observability, maintainability, scalability, and maturity of production ML/AI systems
• Ability to bridge stakeholders across Product, Data Science, MLOps, Data Engineering, and Software Engineering

• Own the technical direction of MLOps capabilities supporting business-critical AI and analytics products
• Define and evolve MLOps principles, deployment standards, model lifecycle practices, monitoring expectations, and reliability requirements
• Review proposed MLOps architectures, deployment approaches, model serving patterns, integration designs, and monitoring concepts
• Ensure ML and AI solutions are scalable, maintainable, observable, secure, reliable, production-ready, and aligned with product goals
• Help teams make pragmatic decisions around model deployment, versioning, CI/CD, rollback, retraining, monitoring, observability, and operational support
• Collaborate with product owners and business stakeholders to understand product priorities, issues, user expectations, operational needs, and evolution
• Translate business needs, stakeholder expectations, and product priorities into clear MLOps and technical direction
• Explain technical risks, constraints, dependencies, options, and trade-offs clearly in business terms
• Facilitate communication between business and technical teams to clarify requirements, incidents, priorities, dependencies, or constraints
• Align multiple teams around shared MLOps decisions, standards, and long-term product needs
• Identify technical risks, reliability gaps, integration challenges, operational bottlenecks, and areas requiring improvement
• Support teams in planning and prioritizing MLOps improvements aligned with long-term product vision
• Promote engineering excellence, standardization, operational discipline, maintainability, observability, and reliability across the AI product landscape
• Support integration of AI services, APIs, data pipelines, ML components, model serving solutions, and business-facing application layers
• Act as a trusted technical voice in discussions with business and engineering stakeholders