Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Крутая вакансия
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Очень структурированная роль с фокусом на MLOps и четким планом развития на первые месяцы; единственный минус - отсутствие указанной зарплаты.
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Зарплата не указанаИдеальное описаниеСовременный стекЧеткий scopeОгромный охват
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Senior Machine Learning Engineer (AI): Building robust, scalable, and maintainable production ML systems for audience targeting, advertising measurement, and content intelligence with an accent on model productionisation, real-time inference, and reusable MLOps patterns. Focus on designing APIs and feature pipelines, auditing and migrating live models, and improving reliability, reproducibility, and lifecycle management at high data volume.
Location: Holborn, London, with occasional travel to Leicester Square
Company
hirify.global operates media and entertainment platforms whose data products support audience targeting, advertising measurement, and content intelligence.
What you will do
Translate experimental machine learning and deep-learning models into reliable, testable production systems.
Build APIs, model-serving infrastructure, and consistent feature pipelines for batch and real-time inference.
Audit and migrate existing production models to modern standards without disrupting live products.
Define engineering standards for model development, testing, deployment, and lifecycle management in partnership with MLOps.
Create reusable templates, components, and documentation that enable data scientists to work independently.
Partner with Data Science, MLOps, Data Engineering, and Product to shape deployable and maintainable products.
Requirements
Senior-level production machine learning experience delivering ML and deep-learning projects in high-volume commercial environments.
Strong Python skills and experience with PyTorch or similar machine learning frameworks.
Experience owning deployment, CI/CD, monitoring, and machine learning lifecycle management.
Understanding of model evaluation, production inference patterns, reproducible environments, and engineering trade-offs.
Experience with Docker, MLflow or an equivalent tool, AWS, SageMaker, Snowflake, Spark or Databricks, and Kubernetes.
Reliability, maintainability, and continuous improvement mindset.
Culture & Benefits
Work on AI and data products influencing what millions of listeners hear and how brands invest their media budgets.
Shape engineering standards and reusable patterns across the data product portfolio.
Collaborate across Data Science, MLOps, Data Engineering, and Product.
Inclusive workplace with reasonable adjustments available throughout the recruitment process and workplace.
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