MLOps Platform Technical Owner

O projekcie:
At hubQuest, we build and scale advanced data and analytics hubs for global organizations, 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 scaling a Global Analytics unit — a centralized, international team building smart data and AI-powered products for daily business operations.

We are looking for a senior MLOps practitioner who has built, deployed, monitored, and operated production ML/AI systems, and is now ready to take 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 people management position.

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 must be able to challenge proposed solutions, assess production readiness, understand trade-offs in model serving, CI/CD, monitoring, retraining, integration, scalability, and support — and explain them clearly to Product Owners, business stakeholders, and technical teams.

The best fit is someone who has earned 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.

The Global Analytics team builds AI-powered capabilities such as forecasting, optimization, recommendation engines, intelligent alerts, route optimization, business insights, and advanced data products supporting global 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.

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.

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

Your impact will come from connecting business expectations with engineering reality and ensuring that product evolution is guided by strong MLOps judgment and real operational priorities.

Role Breakdown

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

What We Offer

- high-impact analytics, MLOps, and AI initiatives;- technical ownership of business-critical AI-powered solutions;- strong interaction with business stakeholders, Product Owners, and technical teams;- influence on MLOps architecture, engineering standards, deployment approaches, monitoring, reliability, and product direction;- continuous learning, certifications, knowledge-sharing, and online courses.

Apply Now

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.

Wymagania:
This Role Is a Strong Fit If You Have

- built, deployed, monitored, or operated production ML/AI systems beyond prototypes, notebooks, or demos;- hands-on experience with model deployment, versioning, registry, monitoring, rollback, retraining, and production support;- strong background in MLOps, ML Engineering, Platform Engineering, Software Engineering, Solution Architecture, or production AI systems;- experience reviewing MLOps architectures and guiding teams through deployment, scalability, reliability, and lifecycle decisions;- strong Python-based engineering background, even though this role does not involve daily coding;- solid understanding of CI/CD for ML, model lifecycle management, reproducibility, observability, monitoring, reliability, and production readiness;- experience with cloud-native AI, ML, data, or software architectures;- ability to work with Product Owners, Data Scientists, Data Engineers, MLOps Engineers, Software Engineers, and business stakeholders;- ability to translate business needs into MLOps direction and technical constraints into business-friendly explanations;- confidence in challenging assumptions, assessing trade-offs, and connecting technical choices with business value, reliability, scalability, and maintainability.

Required Experience

- 5+ years of 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 operationalizing 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, strong stakeholder management, communication skills, and fluent English.

Technology Expectations

Practical experience with several of the following:

- MLflow or equivalent model registry / experiment tracking tooling;- Azure Machine Learning, Databricks, AWS SageMaker, GCP Vertex AI, or equivalent cloud ML platform;- Docker, Kubernetes, CI/CD, infrastructure automation, and cloud-native deployment;- batch and real-time inference, model serving, and API-based integration;- model monitoring, drift detection, quality monitoring, observability, alerting, and dashboards;- rollback, retraining, versioning, reproducibility, and production support;- Python, FastAPI or equivalent backend/service frameworks;- Spark, PySpark, Databricks, Airflow, ADF, or equivalent.

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

This Role Is Probably Not a Fit If Your Experience Is Mainly

- chatbots, RAG demos, LLM API integrations, occasional AI features, Data Science or ML research without production deployment, monitoring, and support;- DevOps or cloud infrastructure without model lifecycle, monitoring, or MLOps ownership;- Product, Project, BA, delivery, or people management without deep hands-on MLOps / ML Engineering background;- junior or mid-level AI engineering without senior-level technical ownership.

Nice to Have

- experience 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 or data products used by business stakeholders;- experience improving reliability, observability, maintainability, scalability, and maturity of production ML/AI systems;- ability to bridge stakeholders, Product, Data Science, MLOps, Data Engineering, and Software Eng.

Codzienne zadania:
- 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.