MLOps Product Technical Lead / Technical Owner

We are a team of experts bringing together top talent in IT, analytics, and advanced technology. Our mission is to provide innovative solutions through our flagship service: building tech teams from scratch and expanding existing units to help our partners become truly data-driven organizations.

Currently, we are looking for an MLOps Product Technical Lead / Technical Owner to support our partner in developing a Global Analytics unit — a centralized team dedicated to strengthening data-driven decision-making and creating smart data products for day-to-day business operations.

This role focuses on taking technical ownership of MLOps capabilities supporting complex AI and analytics products. It combines MLOps expertise, product-oriented thinking, stakeholder collaboration, cross-team alignment, and technical direction-setting.

We are looking for an experienced MLOps-oriented technical leader who has already gained strong hands-on experience in MLOps, ML Engineering, Platform Engineering, Software Engineering, or production AI systems, and is now ready to create impact through product-oriented technical ownership rather than day-to-day implementation.

This is a highly interactive, stakeholder-facing role at the intersection of MLOps, product development, business needs, and engineering delivery. You will work closely with Product Owners, business stakeholders, Data Scientists, Data Engineers, ML Engineers, MLOps Engineers, Software Engineers, and other teams to understand product priorities, clarify requirements, discuss reported issues, assess technical options, and shape the future direction of MLOps capabilities within AI-powered products.

This is not a hands-on coding role and not a classic people management position. Your role will be to ensure that MLOps solutions are not only technically sound, scalable, observable, and reliable, but also aligned with product goals, user needs, business priorities, and long-term product evolution.

About the Team

The Global Analytics team is an innovative and diverse group of Data Scientists, Data Engineers, ML Engineers, MLOps Engineers, Business Intelligence Specialists, Software Developers, UX Designers, and other experts, with a presence across three continents and five countries.

The team drives innovation and reliability while helping the organization become a truly data-driven enterprise.

One of the products developed by the Global Analytics team is an AI-powered sales and analytics ecosystem — a sophisticated, multi-module product suite delivering intelligent capabilities that support global commercial operations and business decision-making.

Examples of capabilities include:

• Consumer behavior change alerts identifying where immediate actions should be taken

• Route optimization supporting field and sales teams

• Product recommendation engines

• Forecasting and optimization solutions

• Intelligent insights supporting sales effectiveness

• Other advanced AI-powered capabilities supporting commercial decision-making

As these analytics capabilities continue to scale globally, we are looking for a technical leader who can own the MLOps direction of this complex product landscape, work effectively with multiple stakeholder groups, and ensure that ML and AI solutions are scalable, reliable, maintainable, observable, and aligned with business and product needs.

Important Role Positioning

This is a MLOps-focused technical leadership and product ownership role, not a hands-on coding role and not a classic line management position.

The role is best suited for someone who has previously gained strong, practical hands-on experience in MLOps, ML Engineering, Platform Engineering, Software Engineering, Solution Architecture, or production AI systems, and is now ready to move into a role focused on MLOps product direction, solution ownership, stakeholder collaboration, cross-team coordination, architecture guidance, and engineering standards.

You will not be expected to deliver production code on a daily basis or act as an individual contributor responsible for implementation. At the same time, this is not a formal people management role focused on hiring, performance reviews, career development, or line management responsibilities.

Some technical coordination, influence, and team guidance will be part of the role, but the core responsibility is technical and product-oriented ownership of MLOps capabilities — not managing people as a line manager.

A key part of this role is interaction. You will spend a significant amount of time working with people: understanding business expectations, clarifying product requirements, discussing issues, aligning priorities, explaining technical constraints, challenging assumptions, and helping different teams move in the same MLOps and product direction.

Your credibility in this role will come from having built, deployed, operated, monitored, or scaled production-grade ML, AI, data, or software systems before. You should be able to understand MLOps details deeply, challenge proposed solutions, assess architectural and operational trade-offs, and guide engineering teams based on real practical experience.

At the same time, your success will depend on your ability to communicate clearly, build trust with stakeholders, facilitate discussions, and translate between business needs, product goals, and MLOps realities.

You should feel comfortable stepping away from daily implementation work and taking responsibility for:

• Product-oriented MLOps technical direction and ownership

• MLOps capabilities embedded in business-critical AI and analytics products

• Model deployment, lifecycle, monitoring, reliability, and operationalization standards

• Stakeholder collaboration and business-facing technical communication

• Cross-team alignment between Product, Business, Data Science, Data Engineering, MLOps, Software Engineering, and other teams

• Requirements clarification and technical interpretation of business and product needs

• Architecture guidance and engineering standards in the MLOps domain

• Technical decision-making and solution reviews

• Challenging assumptions and assessing trade-offs

• Explaining technical risks, constraints, dependencies, and options in a clear and business-oriented way

• Supporting teams in solving complex scalability, reliability, integration, deployment, monitoring, and delivery challenges

• Helping stakeholders understand what is technically feasible, what requires trade-offs, and what should be prioritized

• Ensuring that MLOps decisions support long-term product evolution and real business value

This role is ideal for someone who wants to apply strong hands-on MLOps experience at the product level: shaping how MLOps capabilities should support business-critical AI products, rather than personally implementing every technical component.

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 used across global markets

• 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 products supporting real-world commercial 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

• Casual atmosphere with no unnecessary corporate bureaucracy

• Continuous learning opportunities, certifications, knowledge-sharing initiatives, and online courses

Responsibilities

• 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 your previous hands-on experience to guide teams effectively, assess technical quality, and make pragmatic MLOps decisions — without being responsible for 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

Qualifications and Experience

• Advanced degree in Computer Science, Engineering, Mathematics, Data Science, or a 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, even though this role does not involve daily coding

• Strong understanding of machine learning concepts and experience 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 environments

• Experience working with MLOps capabilities that support business-facing AI, analytics, or data products

• Experience with tools and technologies 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 services such as 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 working with large-scale data environments, for example Databricks, PySpark, distributed data processing, or similar 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

• 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 both technical and non-technical audiences

• Product-oriented mindset and ability to connect technical decisions with business value, usability, reliability, and long-term product evolution

• Professional, service-oriented, ownership-driven mindset

• Fluent English

Nice to Have

• Experience acting as a MLOps Technical Lead, Technical Owner, Product Technical Lead, Solution Architect, Engineering Lead, ML Platform Lead, or similar role

• Experience in stakeholder-facing technical roles involving business requirements, product evolution, issue clarification, or cross-team alignment

• 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, 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, aligned with business needs, understandable for stakeholders, maintainable in production, 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 hands-on coding or individual feature delivery

• No formal line management as the core responsibility

This role requires someone who has already been hands-on and technical enough to earn credibility with engineering teams, especially in the MLOps domain, but who is now ready to create impact through MLOps ownership, product-oriented direction-setting, stakeholder collaboration, and solution leadership rather than individual coding.

If you enjoy MLOps technical leadership, product-oriented solution ownership, stakeholder collaboration, cross-team communication, and driving engineering excellence without being the primary hands-on coder or a formal people manager, we would love to hear from you.

Apply now!

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