MLOps Platform Technical Owner

At hubQuest, we build and scale advanced data and analytics hubs for global organizations, partnering with enterprise clients to design, launch, and evolve core data capabilities. The role supports a Global Analytics unit focused on building smart data and AI-powered products for day-to-day 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, and scaling production-grade ML, AI, data, or software solutions.
• Strong understanding of machine learning concepts and operationalizing ML/AI in production.
• Experience with model lifecycle management, deployment patterns, monitoring, CI/CD, observability, reliability, reproducibility, and support.
• Proficient in Python-based software engineering.
• Experience with cloud-native deployment of AI, ML, data, or software systems.
• Experience with containerization and DevOps tools such as Docker, Kubernetes, CI/CD pipelines, and infrastructure automation.
• Experience with large-scale data environments like Databricks, PySpark, distributed data processing, or data pipelines.
• Strong understanding of software architecture, system integration, APIs, engineering design patterns, and production-grade delivery.
• Experience working in Agile environments with cross-functional teams.
• Strong stakeholder management and communication skills.
• Fluent English and C1 level Polish.

• Own the technical direction of MLOps capabilities supporting AI and analytics products.
• Define and evolve MLOps principles, deployment standards, model lifecycle practices, monitoring, and reliability requirements.
• Review MLOps architectures, deployment approaches, model serving patterns, integration designs, and monitoring concepts.
• Ensure ML and AI solutions are scalable, maintainable, observable, secure, reliable, and production-ready.
• Help teams make decisions around model deployment, versioning, CI/CD, rollback, retraining, monitoring, and operational support.
• Collaborate with Product Owners and stakeholders to understand priorities and operational needs.
• Translate business needs into clear MLOps and technical directions.
• Explain technical risks, constraints, options, and trade-offs clearly.
• Facilitate communication between business and technical teams.
• Align multiple teams around shared MLOps decisions and long-term product needs.
• Identify technical risks, reliability gaps, and operational bottlenecks.
• Support planning and prioritization of MLOps improvements.
• Promote engineering excellence, standardization, operational discipline, maintainability, observability, and reliability.
• Support integration of AI services, APIs, data pipelines, ML components, and business application layers.
• Act as a trusted technical voice with business and engineering stakeholders.

The data controller for the recruitment process is HUBQUEST spółka z ograniczoną odpowiedzialnością based in Warsaw. Personal data will be processed according to GDPR with rights to withdraw consent, access, rectification, and complaint. Providing personal data is required to participate in recruitment. Data will not be transferred outside the country or subject to automated processing. Specific legal clauses related to personal data processing and recruitment are included in the application instructions.