Software Engineer (Machine Learning - MLOps)

About the role

Join the Data Science Engineering team as a Machine Learning Engineer. Work with engineers, data scientists, product managers, systems engineers and analytics professionals to deliver scalable products that improve customer service and operations.

Work across Engineering and Data Science teams on projects spanning supply chain, logistics, stores and online products. Areas include operations optimisation, commercial decision support, forecasting, range optimisation, supply-chain support, search, recommendations and computer vision. Machine Learning Engineers support tool and platform development, code optimisation and deployment to edge, cloud and big-data environments.

Hybrid working

This role requires you to be based in or near Kraków. You will spend 60% of your week (3 days) collaborating with colleagues at office locations or local sites, with the remainder worked remotely.

Benefits

• Permanent contract from the start
• MacBook
• Certified technical training and learning platforms, including Udemy
• Referral bonus
• Sports activities with a personal trainer in the office
• Additional 4 days of paid leave to support well-being and family life
• Up to 20% yearly salary bonus based on individual and business performance
• Private healthcare (LuxMed)
• Cafeteria and Multisport
• Holiday pool expanded from 20 to 25 days for colleagues not yet eligible for full holiday entitlement
• IP Tax Deductible Costs

You will be responsible for

• Participating in group discussions on system design and architecture
• Working with product teams to communicate and translate needs into technical requirements
• Working alongside Data Scientists, Software Engineers and Product teams across the software lifecycle
• Delivering high-quality code and solutions into production
• Performing code reviews to optimise the technical performance of data science solutions
• Supporting production systems, resolving incidents and performing root-cause analysis
• Evolving and improving technology, processes and practices
• Sharing knowledge with the immediate engineering team
• Collaborating with other teams on complex initiatives through working groups
• Improving and building solutions using GenAI
• Applying SDLC practices to create and release robust software

You will need

• An Engineering or Data Science background and a good understanding of the data science toolkit, including programming, machine learning and MLOps
• Experience bringing data science solutions into production
• A higher degree in engineering, computer science, maths or science
• Customer focus and the ability to balance outcome delivery with technical excellence
• Ability to apply technical skills to real-world business problems
• Demonstrable experience building MLOps systems to market best practices
• Commercial experience contributing to high-impact data science projects in complex organisations
• An analytical mindset and ability to tackle specific business problems
• Experience with programming languages and strong proficiency in at least one; Python is preferred
• Experience with version control (Git) and software-lifecycle tooling
• Experience with monitoring, logging and alerting tooling, such as Splunk or Grafana
• Understanding of common data structures and algorithms
• Experience with open-source data science environments
• Knowledge of open-source big-data technologies such as Apache Spark
• Experience building cloud-based solutions; Azure is preferred
• Experience with software development methodologies including Scrum and Kanban

Awareness of emerging MLOps practices and tooling, such as feature stores and model lifecycle management, is advantageous. A background or strong understanding of retail, logistics or ecommerce is advantageous but not required.