MLOps Platform Technical Owner

About hubQuest

At hubQuest, we build and scale advanced data and analytics hubs for global organizations. We partner with enterprise clients to design, launch, and evolve core data capabilities, enabling scalable analytics, advanced AI use cases, and data-driven decision-making across regions.

Currently, we are looking for an MLOps Platform Technical Owner to support our partner in developing and scaling a Global Analytics unit — a centralized, international team focused on building smart data and AI-powered products for day-to-day business operations.

About the Role

We are looking for a senior MLOps practitioner who has previously built, deployed, monitored, and operated production ML/AI systems, and is now ready to take technical ownership of MLOps capabilities across business-critical AI and analytics products.

This is a deeply technical ownership role. It is not a daily feature-coding role, not a Product Manager role, and not a classic people management position.

You will define and evolve MLOps standards, deployment patterns, model lifecycle practices, monitoring approaches, observability expectations, reliability principles, and production-readiness criteria for ML and AI capabilities used across complex business products.

You must be able to challenge proposed solutions, assess production readiness, understand trade-offs in model serving, CI/CD, monitoring, retraining, integration, scalability, and operational support — and explain those trade-offs clearly to Product Owners, business stakeholders, Data Scientists, Data Engineers, MLOps Engineers, Software Engineers, and other teams.

The best fit is someone who has already earned technical credibility by building and operating production-grade ML, AI, data, or software systems, and now wants to create impact through MLOps ownership, architecture guidance, stakeholder alignment, and product-oriented technical direction.

About the Team

The Global Analytics team is an international group of Data Scientists, Data Engineers, ML Engineers, MLOps Engineers, Business Intelligence Specialists, Software Developers, UX Designers, Product Owners, and other experts, with presence across multiple countries and regions.

The team builds and supports AI-powered analytics capabilities used in business-critical decision-making. These may include forecasting, optimization, recommendation engines, intelligent alerts, route optimization, business insights, and other advanced data products supporting global commercial operations.

Your role is not to personally build every model or AI feature. Your role is to own the MLOps direction that allows such capabilities to be deployed, monitored, maintained, governed, and scaled reliably across products, teams, and markets.

Role Positioning

This role sits at the intersection of:
• MLOps and ML Engineering
• AI product operationalization
• production reliability and observability
• architecture guidance
• technical standards
• stakeholder communication
• cross-team alignment
• product-oriented technical ownership

You will work closely with Product Owners, business stakeholders, Data Science teams, Data Engineering teams, MLOps teams, Software Engineering teams, and platform/infrastructure teams.

You will not be expected to deliver production code every day. However, this is still a highly technical role. You must be able to read and challenge technical designs, understand pipeline-level and architecture-level trade-offs, review MLOps implementation approaches, and guide engineers based on real hands-on experience.

This is also not a formal line management role. You may guide teams technically, influence decisions, facilitate alignment, mentor engineers, and support technical planning, but you will not be primarily responsible for hiring, performance reviews, career development, or people management.

What You Will Own

You will take ownership of the MLOps direction for complex AI and analytics products deployed across global business environments.

Your responsibilities will include:
• owning the technical direction of MLOps capabilities supporting business-critical AI and analytics products;
• defining and evolving MLOps principles, deployment standards, model lifecycle practices, monitoring expectations, and reliability requirements;
• reviewing proposed MLOps architectures, deployment approaches, model serving patterns, integration designs, and monitoring concepts;
• ensuring that ML and AI solutions are scalable, maintainable, observable, secure, reliable, production-ready, and aligned with product goals;
• helping teams make pragmatic decisions around model deployment, versioning, CI/CD, rollback, retraining, monitoring, observability, and operational support;
• working with Product Owners and business stakeholders to understand product priorities, reported issues, user expectations, operational needs, and expected product evolution;
• translating business needs, stakeholder expectations, and product priorities into clear MLOps and technical direction;
• explaining technical risks, constraints, dependencies, options, and trade-offs in a clear and business-oriented way;
• facilitating communication between business and technical teams when requirements, incidents, priorities, dependencies, or technical constraints need to be clarified;
• aligning multiple teams around shared MLOps decisions, standards, and long-term product needs;
• identifying technical risks, reliability gaps, integration challenges, operational bottlenecks, and areas requiring improvement;
• supporting teams in planning and prioritizing MLOps improvements aligned with the long-term product vision;
• promoting engineering excellence, standardization, operational discipline, maintainability, observability, and reliability across the AI product landscape;
• supporting integration of AI services, APIs, data pipelines, ML components, model serving solutions, and business-facing application layers;
• acting as a trusted technical voice in discussions with both business and engineering stakeholders.

This Role Is a Strong Fit If You Have
• built, deployed, monitored, or operated production ML/AI systems beyond prototypes, notebooks, or demos;
• practical experience with model deployment, model versioning, model registry, monitoring, rollback, retraining, and production support;
• strong hands-on background in MLOps, ML Engineering, Platform Engineering, Software Engineering, Solution Architecture, or production AI systems;
• experience reviewing MLOps architectures or guiding engineering teams through deployment, scalability, reliability, and lifecycle decisions;
• strong Python-based software engineering background, even though this role does not involve daily coding;
• strong understanding of MLOps practices such as CI/CD for ML, model lifecycle management, reproducibility, observability, monitoring, reliability, and production readiness;
• experience working with cloud-native architectures supporting AI-powered or data-driven products;
• experience working with Product Owners, Data Scientists, Data Engineers, MLOps Engineers, Software Engineers, and business stakeholders;
• ability to translate business and product needs into MLOps direction;
• ability to translate technical constraints into business-friendly explanations;
• confidence in challenging assumptions, assessing trade-offs, and guiding technical decisions;
• product-oriented mindset and ability to connect technical decisions with business value, usability, reliability, scalability, and long-term maintainability.

Required Experience
• 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 understanding of machine learning concepts and experience operationalizing ML or AI solutions in production environments;
• experience with MLOps practices such as model lifecycle management, deployment patterns, monitoring, CI/CD, observability, reliability, reproducibility, and production support;
• experience with Python-based software engineering;
• experience with cloud-native deployment of AI, ML, data, or software systems;
• experience with containerization and DevOps technologies such as Docker, Kubernetes, CI/CD pipelines, and infrastructure automation;
• experience working with large-scale data environments, for example Databricks, PySpark, distributed data processing, data pipelines, or equivalent technologies;
• 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;
• fluent English.

Technology Expectations

We do not expect every candidate to have used the exact same tools, but we do expect practical experience with several of the following areas:
• MLflow or equivalent model registry / experiment tracking tooling;
• Azure Machine Learning, Databricks, AWS SageMaker, GCP Vertex AI, or equivalent cloud ML platform;
• Docker and Kubernetes for ML, AI service, or platform deployment;
• CI/CD for ML models, AI services, or data/ML pipelines;
• model serving patterns, batch and real-time inference, API-based model integration;
• model monitoring, data drift, model drift, quality monitoring, observability, alerting, and operational dashboards;
• rollback, retraining, versioning, reproducibility, and production support practices;
• Python, FastAPI or equivalent backend/service frameworks;
• distributed data processing, for example Spark, PySpark, Databricks, Airflow, ADF, or equivalent;
• cloud-native architecture and infrastructure automation.

Experience with LLMOps, RAG evaluation, GenAI observability, LangChain/LangGraph, vector databases, prompt evaluation, or AI agents is welcome, but it is not a substitute for production MLOps experience.

This Role Is Probably Not a Fit If Your Experience Is Mainly
• building chatbots, RAG demos, or LLM API integrations without owning production ML/AI lifecycle;
• full-stack or backend development with only occasional AI features;
• Data Science, ML research, or model development without production deployment, monitoring, and operational support;
• DevOps or cloud infrastructure without model lifecycle, model monitoring, or MLOps-specific ownership;
• Product Management, Project Management, Business Analysis, or delivery management without deep hands-on MLOps / ML Engineering background;
• people management without current ability to challenge MLOps architecture and implementation decisions;
• junior or mid-level AI engineering without senior-level technical ownership;
• AI experimentation without responsibility for scalability, reliability, maintainability, observability, and production support.

Nice to Have
• experience acting as MLOps Technical Lead, MLOps Product Owner, MLOps Platform Lead, ML Platform Lead, Solution Architect, Technical Owner, or similar;
• experience overseeing MLOps capabilities, ML platforms, AI solutions, analytics products, or data products used by business stakeholders;
• experience working with Product Owners or product teams on AI, ML, analytics, or data-driven products;
• experience in global, distributed, or matrix organizations;
• experience working with commercial, sales, supply chain, finance, or business operations analytics;
• experience improving reliability, observability, maintainability, scalability, and operational maturity of existing production ML or AI systems;
• experience supporting teams through technical change, modernization, standardization, or MLOps maturity improvements;
• experience acting as a bridge between business stakeholders, Product, Data Science, MLOps, Data Engineering, and Software Engineering teams.

Business Impact

The solutions supported in this role directly contribute to global business operations by enabling scalable, reliable, observable, and well-governed MLOps capabilities for AI-powered products.

Your work will help ensure that advanced analytics and AI products are not only technically sound, but also properly operationalized, maintainable in production, understandable for stakeholders, and ready to scale across markets.

A major part of your impact will come from connecting business expectations with MLOps and engineering reality: helping stakeholders understand what is possible, helping technical teams understand what the business needs, and ensuring that product evolution is guided by strong MLOps judgment, practical engineering experience, and real operational priorities.

You will help make sure that MLOps capabilities are not treated as isolated technical components, but as an essential part of business-critical AI products that need to deliver value, remain reliable, and evolve with changing product and market needs.

Role Breakdown
•

30% stakeholder collaboration, product alignment, requirements clarification, issue analysis, cross-team alignment, and product evolution discussions;
• 30% MLOps technical ownership, solution direction, technical decision-making, and architecture guidance;
• 25% MLOps standards, solution reviews, deployment approaches, monitoring practices, reliability, lifecycle management, and team guidance;
• 15% technical discovery, product-oriented risk analysis, dependency clarification, and strategic technical planning;
• no expected day-to-day feature coding or individual feature delivery;
• no formal line management as the core responsibility.

What We Offer
• high-impact projects involving advanced analytics, MLOps, and AI initiatives;
• opportunity to work in a global and diverse team with international reach;
• product-oriented technical ownership of MLOps capabilities supporting business-critical AI-powered solutions;
• strong interaction with business stakeholders, Product Owners, and multiple technical teams;
• opportunity to influence MLOps architecture, engineering standards, deployment approaches, monitoring practices, operational reliability, and long-term product direction;
• work on sophisticated AI and analytics products supporting real-world business operations;
• exposure to large-scale, production-grade ML and AI systems operating across global markets;
• close collaboration with experienced Data Science, Data Engineering, MLOps, Software Engineering, Product, and Business teams;
• opportunity to act as a technical bridge between business needs, product priorities, and MLOps/engineering delivery;
• continuous learning opportunities, certifications, knowledge-sharing initiatives, and online courses.

Apply Now

If you have already built and operated production ML/AI systems, understand the practical realities of MLOps, and want to create impact through technical ownership, architecture guidance, stakeholder collaboration, and product-oriented MLOps direction rather than daily feature coding, we would love to hear from you.

Please add to your CV the following clause:
"I hereby agree to the processing of my personal data included in my job offer by hubQuest spółka z ograniczoną odpowiedzialnością located in Warsaw for the purpose of the current recruitment process.”

If you want to be considered in the future recruitment processes please add the following statement:
"I also agree to the processing of my personal data for the purpose of future recruitment processes.”