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Faculty

Machine Learning Engineer

Faculty

. Build and deploy production-grade machine learning software, tools, and infrastructure .

Posted 10/8/2026full-timeLondon • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying machine learning software and tools, with a strong focus on operationalizing models using Python and frameworks like Scikit-learn, TensorFlow, or PyTorch. Capable of collaborating with cross-functional teams to translate complex machine learning concepts into actionable business strategies.

Highest-signal resume keywords
Machine Learning LifecyclePython ProgrammingCloud Platforms (AWS, Azure, GCP)Containerization (Docker, Kubernetes)Software Engineering Best Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Machine LearningModel OperationalizationPythonScikit-learnTensorFlowPyTorchCloud InfrastructureDockerKubernetesStatistical Analysis
Soft Skills
Excellent CommunicationAdvising Non-Technical Stakeholders
Industry Keywords
RetailConsumerEcommerceMarketingSupply ChainCustomer Data

Tech Stack

Tools & technologies
AWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact
  • Build and deploy production-grade machine learning software, tools, and infrastructure
  • Create reusable, scalable solutions for AI and machine learning systems across retail and consumer use cases
  • Collaborate with engineers, data scientists, product teams, and commercial leads on client challenges
  • Lead technical scoping and architectural decisions for project feasibility, scalability, and commercial impact
  • Define and implement standards for deploying machine learning systems in production
  • Advise customers and partners by translating complex ML concepts into actionable business outcomes
  • Apply machine learning to demand forecasting, customer analytics, personalisation, pricing, marketing optimisation, inventory management, and operational efficiency
  • Support clients in adopting AI capabilities that improve customer experiences and drive sustainable growth
  • Participate in Talent Team Screen, Pair Programming, System Design, and Commercial interviews

Requirements

What you’ll need
  • Understanding of the full machine learning lifecycle
  • Experience operationalising models built with Scikit-learn, TensorFlow, or PyTorch
  • Strong Python skills
  • Solid experience with software engineering best practices
  • Hands-on experience with cloud platforms and infrastructure such as AWS, Azure, or GCP, including architecture and security
  • Experience with containerisation and orchestration tools such as Docker and Kubernetes
  • Understanding of probability, statistics, experimentation, and common machine learning techniques
  • Experience working with retail, consumer, ecommerce, marketing, supply chain, or customer data is beneficial but not essential
  • Excellent communication skills and ability to advise non-technical stakeholders

Benefits

Comp & perks
  • The company is open to conversations about part-time hours
  • AI note-taker use in interviews can be opted out of
  • Diverse and inclusive workplace emphasizing applications from people of all backgrounds