Software Engineer (Machine Learning - MLOps)

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NieokreśloneHybryda·KrakówFull-time

#471103·Dodano 5 dni temu·1

Źródło: Tesco Technology

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Tech Stack / Keywords

MLOpsPython Git Splunk Grafana Apache Spark Azure GenAIfeature storesmodel lifecycle management

Podsumowanie oferty

Join the Data Science Engineering team as a Machine Learning Engineer, working across engineering and data science teams. You’ll help deliver scalable products for customer service and operations, including by improving tools and platforms and bringing data science solutions into production with MLOps and GenAI.

Wymagania

A higher degree in engineering, computer science, mathematics or science.
Customer focus and the ability to balance outcome delivery with technical excellence.
Ability to apply technical skills and know-how to real-world business problems.
Demonstrable experience building MLOps systems according to market standards.
Commercial experience contributing to high-impact Data Science projects within complex organisations.
An analytical mindset and ability to tackle specific business problems.
Experience with different programming languages and a good grasp of at least one; the ideal candidate is fluent in Python.
Use of version control (Git) and related software lifecycle tooling.
Experience with monitoring, logging and alerting tools such as Splunk or Grafana.
Understanding of common data structures and algorithms.
Experience working with open-source Data Science environments.
Knowledge of open-source big-data technologies such as Apache Spark.
Experience building solutions that run in the cloud, ideally Azure.
Experience with software development methodologies including Scrum and Kanban.

Nice to have:

Awareness of emerging MLOps practices and tooling, such as feature stores and model lifecycle management.
A background or strong understanding of the retail sector, logistics and/or ecommerce.

Obowiązki

Participate in group discussions on system design and architecture.
Work with product teams to communicate needs and translate them into technical requirements.
Work alongside Data Scientists, Software Engineers and Product teams across the software lifecycle.
Deliver high-quality code and solutions, bringing solutions into production.
Perform code reviews to optimize the technical performance of data science solutions.
Support production systems, resolve incidents and perform root cause analysis.
Look for ways to evolve and improve technology, processes and practices.
Share knowledge with the immediate engineering team.
Collaborate with other teams on complex initiatives through working groups.
Improve and build solutions using GenAI.
Apply SDLC practices to create and release robust software.

Tesco Technology

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