MLOps Product Technical Lead / Technical Owner

hubQuest is a company focused on building tech teams and expanding existing units 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 business operations. The Global Analytics team includes Data Scientists, Data Engineers, ML Engineers, MLOps Engineers, Business Intelligence Specialists, Software Developers, UX Designers, and other experts across multiple continents and countries.

• Advanced degree in Computer Science, Engineering, Mathematics, Data Science, or related STEM field
• 5+ years of practical 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 hands-on background in Python-based software engineering
• Strong understanding of machine learning concepts and operationalizing ML or AI solutions at scale
• Strong understanding of MLOps practices including model lifecycle management, deployment patterns, monitoring, CI/CD, observability, reliability, reproducibility, and production support
• Ability to assess MLOps decisions, review solution quality, challenge assumptions, and guide engineering teams based on real implementation experience
• Experience designing, deploying, scaling, or technically overseeing complex AI-driven applications in production
• Experience with MLOps capabilities supporting business-facing AI, analytics, or data products
• Experience with tools such as MLflow, TensorFlow, PyTorch, or equivalent ML/MLOps ecosystems
• Good understanding of cloud-native architectures supporting AI-powered business products
• Strong Azure experience and familiarity with Azure Machine Learning, Databricks, Azure Data Factory, Azure Functions, Azure DevOps, or equivalent cloud tools
• Good understanding of DevOps technologies such as Docker and Kubernetes
• Experience with large-scale data environments like Databricks, PySpark, or distributed data processing
• Strong understanding of software architecture, system integration, APIs, engineering design patterns, and production-grade delivery practices
• Experience working in Agile environments and collaborating with cross-functional product and engineering teams
• Strong stakeholder management and communication skills
• Ability to work effectively with business stakeholders, Product Owners, technical teams, and senior technical contributors
• Ability to translate business and product needs into MLOps and technical direction, and technical constraints into business-friendly explanations
• Ability to facilitate discussions, clarify ambiguity, align expectations, and support decision-making across multiple teams
• Confidence in discussing requirements, issues, risks, priorities, product needs, and technical trade-offs with technical and non-technical audiences
• Product-oriented mindset connecting technical decisions with business value, usability, reliability, and long-term product evolution
• Professional, service-oriented, ownership-driven mindset
• Fluent English

• Own the MLOps technical direction and operational reliability of complex AI-driven business products deployed globally
• Act as a technical owner for MLOps capabilities across a multi-module AI and analytics product ecosystem
• Shape how MLOps capabilities support product goals, user needs, business priorities, and long-term product evolution
• Define and evolve MLOps principles, deployment standards, model lifecycle practices, monitoring approaches, and operational reliability expectations
• Serve as a key technical partner for Product Owners, business stakeholders, Data Science teams, Data Engineering teams, MLOps teams, Software Engineering teams, and other internal groups
• Work closely with business stakeholders to understand priorities, product requirements, reported issues, operational needs, user expectations, and expected product evolution
• Translate business needs, user problems, stakeholder expectations, and product priorities into clear MLOps and technical direction
• Facilitate communication between business and technical teams when requirements, incidents, priorities, dependencies, or technical constraints need to be clarified
• Help stakeholders understand MLOps-related options, limitations, risks, dependencies, trade-offs, and recommended directions
• Align multiple teams around shared MLOps decisions, product direction, engineering standards, and long-term business needs
• Provide technical leadership and guidance without acting as a formal people manager
• Guide engineering teams through MLOps decisions, architectural trade-offs, deployment patterns, integration approaches, and solution design choices
• Review proposed technical solutions, architectural designs, model deployment approaches, monitoring concepts, integration patterns, and critical implementation decisions
• Challenge assumptions and ensure that proposed solutions are scalable, maintainable, observable, secure, reliable, production-ready, and product-aligned
• Support teams in planning and prioritizing MLOps improvements aligned with the long-term product vision
• Help identify technical risks, dependencies, bottlenecks, integration challenges, reliability gaps, product constraints, and areas requiring improvement
• Promote engineering excellence, standardization, reliability, observability, maintainability, and operational discipline across the AI product landscape
• Support teams in integrating AI services, APIs, data pipelines, ML components, model serving solutions, and business-facing application layers
• Ensure appropriate practices for deployment, monitoring, lifecycle management, reliability, incident analysis, and operational support of ML and AI solutions
• Use previous hands-on experience to guide teams effectively, assess technical quality, and make pragmatic MLOps decisions without day-to-day implementation
• Act as a trusted technical voice in discussions with both business and engineering stakeholders
• Ensure that MLOps decisions are connected to product value, business usability, scalability, and long-term maintainability

The Controller of your personal data being processed for the purpose of the recruitment process is HUBQUEST spółka z ograniczoną odpowiedzialnością, ul. Ksawerów 3, 02-656 Warszawa. Contact with Data Protection Officer is possible via e-mail at [email protected]. Your personal data will be processed for the purpose of the recruitment process on the basis of the given consent (article 6 point 1a of the Regulation (EU) 2016/679). You have the right to withdraw given consent at any time not affecting the lawfulness of processing based on consent before its withdrawal. Personal data will be stored for a period not exceeding three years after the date of its obtainment, or until the consent is withdrawn. In case you agree to participate in future recruitment processes your personal data will be stored for a maximum period of six years following your application, or until the consent is withdrawn. Expected categories of data recipients are: HR department employees and hiring managers, IT service providers, recruitment service providers. You have the right to access and request for the rectification or erasure of your personal data. Request for the erasure of data is tantamount to resignation from participation in recruitment process. Furthermore you have the right to request for the limitation of processing in the cases referred to in article 18 of the GDPR, and the right to data portability. You have the right to lodge a complaint with the President of the Personal Data Protection Office. It is not obligatory to provide data in application documents but it is a condition allowing participation in recruitment process. Personal data will not be transferred to a third country. Data you provided will not be a subject to automated processing and profiling.