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Senior Machine Learning Engineer (AI)

Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
UK
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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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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