ML Engineer

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NieokreśloneHybryda·WarsawFull-time

#477832·Dodano 5 dni temu·0

Źródło: Xebia

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Tech Stack / Keywords

Python SQLMLOpsGCP CI/CDVertex AIBigQueryVertex PipelinesDaskSpark

Podsumowanie oferty

Build, deploy and maintain production-grade machine learning services and specialised tooling for models in the advertising domain. Design and operate ML engineering workflows using Python, SQL, MLOps practices and cloud platforms, preferably Google Cloud Platform (GCP).

Wymagania

Strong software engineering fundamentals and experience writing production-quality, modular and maintainable code.
Proven experience deploying and operating machine learning models in production environments.
Hands-on experience with automated monitoring, alerting and production troubleshooting.
Advanced knowledge of MLOps, including model lifecycle automation, CI/CD pipelines and release management.
Practical experience with cloud platforms, preferably Google Cloud Platform (GCP), supporting large-scale data and ML workloads.
Strong Python and SQL skills in machine learning engineering contexts, including training orchestration and inference support.
Experience with containerised services and production deployment environments.
Solid understanding of DevOps principles and SRE practices applied to machine learning systems.
Familiarity with AI-assisted coding tools and their practical application in software development.
Ability to collaborate effectively with Data Science, Research and other engineering teams.

Nice to have:

Experience in AdTech, advertising technology, ranking or recommendation systems.
Experience with distributed data processing and feature engineering pipelines, e.g. Vertex Pipelines, Dask or Spark.
Familiarity with cloud-based model serving ecosystems and microservice architectures.
Experience with cross-language model integration patterns.
Knowledge of feature stores and train/serve consistency controls.
Hands-on experience with GCP services such as Vertex AI, BigQuery and Cloud Storage.

Obowiązki

Build, deploy and maintain production-grade machine learning services and specialised tooling for models in the advertising domain.
Design and orchestrate ML engineering workflows, including model packaging, deployment strategies, observability and operational stability.
Productionise machine learning models in collaboration with Data Science and Research teams.
Develop and maintain automated deployment pipelines, model monitoring, alerting and rollback strategies.
Ensure reproducible model packaging and reliable model lifecycle management.
Support production ML systems through troubleshooting, root cause analysis and continuous improvements.
Apply DevOps and Site Reliability Engineering (SRE) principles to improve the reliability, scalability and maintainability of ML services.
Use AI-assisted coding tools to improve engineering productivity while maintaining high code quality.

Inne informacje

Availability to work in a hybrid model from Warsaw and participate in in-person project meetings when required.
Recruitment process: CV review, HR call, interview, client interview and decision.

Xebia sp. z o.o.

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