ABOUT THE COMPANY

We are an end-to-end data services partner to global enterprises, founded in 2008 and headquartered in Warsaw. Our teams work with over 75 leading consumer packaged goods brands across more than 30 countries, helping them unlock the full value of their data — from strategy and development through to operations and adoption.

Our work spans supply chain analytics, customer analytics, AI and machine learning, data platforms, and digital commerce. We are recognised as a Strong Performer in the Gartner Peer Insights Voice of the Customer report for data and analytics, and hold Great Place to Work certification in multiple countries.

ABOUT THE ROLE

You'll join the AI Engineering team within our Data Science and AI practice, working at the intersection of ML engineering, MLOps, and GenAI delivery. The focus of this role is taking machine learning models from development into production — reliably, repeatably, and at scale.

You'll collaborate closely with Data Science teams to productionise their work, design and deliver GenAI solutions, and define the frameworks and best practices that underpin how the team operates. Client-facing communication is part of the role — you'll be expected to present solutions and concepts clearly to both internal and external stakeholders.

WHAT YOU'LL WORK ON

ML model productionisation

Work with Data Science teams to implement machine learning models into production, ensuring solutions are robust, scalable, and maintainable.

GenAI solution design and delivery

Design and deliver GenAI solutions end-to-end, with a focus on practical, innovative implementations of LLM and ML automation for scale and efficiency.

Industrialised processing pipelines

Design, deliver, and manage large-scale processing pipelines that support production AI and ML workloads across enterprise environments.

MLOps and LLMOps frameworks

Define and implement best practices across the ML model lifecycle. Build out MLOps and LLMOps frameworks and support Data Science teams in adopting them.

Requirements gathering and estimation

Gather technical requirements from stakeholders and produce reliable estimates for planned work.

Client-facing presentation

Present solutions, concepts, and results to both internal teams and external clients. Translate technical detail into clear, accessible communication.

Technical documentation

Produce clear technical documentation covering architecture decisions, implementation details, and operational guidance.

Staying current

Continuously gather and apply knowledge on modern techniques, tools, and frameworks in ML architecture and operations — and bring that knowledge back to the team.

WHAT WE LOOK FOR

5+ years of data engineering experience

Strong foundations in data engineering, with the ability to design and manage pipelines that production AI systems depend on.

5+ years of production Python development

Fluent in writing clean, production-grade Python. Your code is readable, testable, and built to be maintained by others.

3+ years of production ML engineering

Solid hands-on experience building and shipping ML systems in production — covering the full lifecycle from training through to monitoring and maintenance.

1+ year of GenAI experience

Practical experience working with GenAI — LLMs, agents, or related systems — in a production or near-production context.

MLOps and LLMOps tooling

Practical experience with MLOps and LLMOps platforms, particularly AzureML and Azure AI. Comfortable defining and implementing operational best practices, not just following them.

THE TEAM

You'll join a specialist Data Science and AI Engineering practice working alongside experienced data scientists, ML engineers, and cloud engineers. The team delivers solutions for large international clients across CPG, retail, and manufacturing. There is a strong knowledge-sharing culture, with internal communities, competency centres, and structured learning programmes built into how the team operates.

COMPENSATION & BENEFITS

Rate

150 – 165 PLN per hour on a B2B contract, depending on experience.

Work model

Fully remote or office-based — your choice. Flexibility on working hours and contract form.

Workation policy

Option to work remotely from other locations for defined periods.

Onboarding

Comprehensive online onboarding programme with a dedicated buddy from day one.

Learning and development

Unlimited access to the Udemy learning platform from day one. Certificate training programmes, upskilling support, capability development programmes, competency centres, knowledge sharing sessions, community webinars, and over 110 training opportunities per year.

Career growth

Internal promotion pathways — 76% of managers were promoted internally. Cooperation with top-tier engineers and domain experts across the organisation.

Referral bonuses

Financial rewards for successful employee referrals.

Wellbeing

Activities to support health and wellbeing, with opportunities to contribute to charitable causes and environmental initiatives.

Equipment

Modern office equipment provided.

Employer recognition

Great Place to Work certified employer.