Senior ML/AI Engineer

ABOUT THE COMPANY Our client is 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 packed 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.