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Senior DS/ML Engineer

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Senior0Full-time

#480256·Dodano 2 dni temu·1

Źródło: Xebia

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Tech Stack / Keywords

Python SQL BigQueryTensorFlow/KerasPyTorch AirflowdbtSpark Kubernetes LLM

Podsumowanie oferty

Own models end to end for caller identity, business identification, spam and fraud detection, from problem framing through deployment and monitoring. Work on ranking, classification, graph, personalisation and LLM-based models, using Python, SQL and machine-learning technologies.

Wymagania

Several years of hands-on experience building and shipping machine-learning models to production.
Strong Python and SQL skills, with experience working with large-scale data in BigQuery or a similar data warehouse.
Experience with classical machine learning, such as gradient boosting and ranking, and deep learning using TensorFlow/Keras or PyTorch, including training on GPUs.
Experience designing offline evaluations that predicted online results, and running A/B tests.
Experience with data and ML pipelines using Airflow, dbt and Spark, or with running training jobs on Kubernetes.

Nice to have:

Experience gathering requirements from non-technical stakeholders, turning them into a scoped plan, and communicating results and trade-offs clearly.
Experience setting or improving data science standards within a team, including evaluation, reviews and reproducibility.
Experience with graph machine learning, such as GNNs, link prediction or label propagation, or with recommendation and retrieval models, such as two-tower models and embeddings.
Experience using LLMs in production for extraction, classification or generation, including LLM-as-judge evaluation.
Familiarity with MLOps tools such as MLflow for experiment tracking, model registries, versioning and reproducible training.
Experience with NLP on short, multilingual or transliterated text, such as names, or computer vision on user images.
Experience with telecom or calling data, or in fraud detection.

Obowiązki

Own models end to end across caller identity, business identification, spam and fraud detection: problem framing, data, training, evaluation, deployment and monitoring.
Work on ranking, classification, graph, personalisation and LLM-based models.
Set the team’s evaluation standards through historical backtests, honest baselines, false-positive checks on legitimate callers, shadow runs and A/B tests.
Build shared tooling, including reusable evaluation harnesses, experiment tracking, project templates and workflows for moving work from notebooks into production code.
Work with product stakeholders to turn loosely defined problems into clear modelling goals and success metrics, and explain results and trade-offs to non-technical audiences.
Share responsibility for model monitoring and incident handling, including drift detection.
Document decisions and results in short, reproducible analyses.

Inne informacje

Work from the European Union region and a work permit are required.
Recruitment process: CV review, HR call, interview, client interview and decision.

Xebia sp. z o.o.

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