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AND Digital

Machine Learning Engineer – Contract

AND Digital

. Deploy and operate artificial intelligence and machine learning architectures in production ecosystems .

Posted 10/6/2026contractLondon • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

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

Demonstrates expertise in deploying and operating artificial intelligence and machine learning architectures, with proficiency in Python, SQL, and modern modeling toolkits. Capable of constructing high-volume batch processing systems and real-time microservices while effectively collaborating with technical and non-technical stakeholders.

Highest-signal resume keywords
Artificial Intelligence Architecture DeploymentMachine Learning Lifecycle ManagementPython ProgrammingGoogle Cloud Platform TechnologiesContainerization with Docker and Kubernetes

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Machine Learning DevelopmentBatch Processing SystemsReal-Time MicroservicesAutomated Continuous RetrainingSystem ObservabilityPyTorchTensorFlowScikit-learnSQLVersion Control
Soft Skills
Effective CommunicationCollaborationDetail-OrientedInquisitiveSelf-Starting
Tools & Technologies
Google Cloud PlatformBigQueryVertex AIDataflowDockerKubernetesADKLangChainAutoGen
Industry Keywords
Machine Learning EngineerData EngineerGenerative AILarge Language ModelsAgent Orchestration

Tech Stack

Tools & technologies
BigQueryCloudDockerGoogle Cloud PlatformKubernetesMicroservicesPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact
  • Deploy and operate artificial intelligence and machine learning architectures in production ecosystems
  • Construct high-volume batch processing systems and real-time microservices
  • Implement automated continuous retraining and system observability
  • Architect sustainable solutions across the machine learning lifecycle
  • Use Python, SQL, PyTorch, TensorFlow, and Scikit-learn for machine learning development
  • Work with public cloud environments, including Google Cloud Platform technologies such as BigQuery, Vertex AI, and Dataflow
  • Build and manage deployment pipelines, version control, and containerized platforms using Docker and Kubernetes
  • Apply generative AI, Large Language Models, and agent orchestration tools such as ADK, LangChain, or AutoGen
  • Explain technical trade-offs and influence engineering peers and non-technical business partners
  • Collaborate with clients, teammates, and multidisciplinary teams to solve complex technology challenges and deliver better outcomes

Requirements

What you’ll need
  • Hands-on experience deploying and operating artificial intelligence and machine learning architectures in production ecosystems
  • Proficiency in constructing high-volume batch processing systems, real-time microservices, automated continuous retraining, and system observability
  • Deep understanding across the entire machine learning lifecycle
  • Strong technical foundations in Python and SQL
  • Experience with modern modeling toolkits such as PyTorch, TensorFlow, and Scikit-learn
  • Practical expertise with public cloud environments, preferably Google Cloud Platform technologies such as BigQuery, Vertex AI, and Dataflow
  • Skills with deployment pipelines, version control, and containerization platforms such as Docker and Kubernetes
  • Familiarity with generative AI, Large Language Models, and agent orchestration tools such as ADK, LangChain, or AutoGen
  • Ability to articulate technical trade-offs effectively to technical and non-technical stakeholders
  • Inquisitive, self-starting, detail-oriented, and committed to exploring modern machine learning tooling
  • Degree in Computer Science, Data Science, Engineering, or an equivalent technical field is nice to have, not essential
  • At least three years of professional experience as a Machine Learning Engineer or Data Engineer is nice to have, not essential
  • Experience aligning technical model performance with broader business objectives is nice to have, not essential

Benefits

Comp & perks
  • Equal opportunities and diversity and inclusion commitment
  • Application and interview adjustments available
  • Support from the recruitment team for application or interview adjustments