VirtusLab

Python Engineer with MLOps (Senior)

140 - 170 PLN/ godz./ godz.B2BB2B (netto)

SeniorZdalnie·KrakówFull-time·B2B

#464748·Dodano 13 dni temu·2

Źródło: justjoin.it

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Tech Stack / Keywords

PythonCloudIaCGitHub Actions PySpark AirflowObservabilityMLOpsKubernetesProductionize models

Podsumowanie oferty

Develop production-ready machine learning pipelines for the Forecasting & Commodities project, working with data scientists and engineering teams. The work includes deploying and maintaining workflows across Azure and on-premises ETL clusters, using Python, PySpark and Kubernetes.

Wymagania

Solid Python skills and hands-on experience taking projects to production.
Practical experience with orchestration tools such as Airflow.
Background in distributed data processing, especially Spark.
Hands-on experience with Kubernetes.
Cloud experience, preferably Azure, and Docker containerisation.
Experience collaborating with cross-functional teams and contributing to technical design.
Clear and direct communication with technical and business partners.
Readiness to share domain expertise and support other team members’ technical growth.
Independence, pragmatic problem-solving and a clear big-picture view.
English proficiency at a minimum B2/C1 level.

Obowiązki

Key scope of services:

Develop libraries, tools and frameworks to standardise and accelerate end-to-end development and deployment of machine learning models.
Develop model training code in AzureML and build and maintain cloud infrastructure using Infrastructure as Code.
Use distributed data processing tools such as Spark to parallelise machine learning computations across large-scale datasets.
Diagnose and resolve technical issues, delivering high-availability, reusable and adaptable engineering solutions.
Collaborate with engineering and data science teams to provide technical guidance and optimise core workflows.
Promote best practices in code quality, security and scalability.
Make autonomous, data-driven technical decisions aimed at driving business growth.

Project challenges:

Build business forecasting systems by delivering reusable toolsets for data scientists and analysts.
Streamline onboarding, deployment and automated patching pipelines for new machine learning models.
Partner with cross-functional units to enhance end-customer experience through technology integration.
Apply DevOps methodologies to establish reproducible patterns across business domains.

Benefity

Home office reimbursement.
Language lessons.
MyBenefit points.
Private healthcare.
Training package.
In-house training.

Kursy językowe

Opieka zdrowotna

Szkolenia wewnętrzne

Inne informacje

Primarily remote engagement with occasional on-site collaboration, approximately once a month.

VirtusLab

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