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Kensington, London, UK
Actively hiring, first seen by us 5 days agoDesign scalable architectures that turn model outputs into production decisioning systems
Create standards, frameworks and governance processes for production AI
Implement model monitoring, retraining and lifecycle management processes
Advise on frameworks connecting models and decision engines to client CRM and channels
Develop reusable assets, accelerators and best practices
Design and implement scalable architectures that operationalise analytical outputs into production decisioning systems.
Create standards, frameworks and governance processes for production AI.
Implement model monitoring, retraining and lifecycle management processes.
Advise on the framework that connects models and decision engines into:
Develop reusable assets, accelerators and best practices.
Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 5
Early. Similar jobs usually come down between day 6 and day 69.
From 4,284 similar postings that came down. Coming down is not always a hire.
6 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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Experience & Skills Essential Production grade Python experience Considerable experience using AWS Sagemaker Unified Studio, MLflow, Inference endpoints Model monitoring / drift detection (e.g. SageMaker Model Monitor, Evidently, WhyLabs) CI/CD for ML (SageMaker Pipelines, GitHub Actions/GitLab CI, or Azure DevOps) Containerization & orchestration (Docker, Kubernetes/EKS) Feature store (SageMaker Feature Store, Databricks Feature Store, or Tecton) Infrastructure as Code (Terraform or CloudFormation/SAM) A/B testing / experimentation frameworks Data privacy/governance awareness (GDPR, consent management) Experience deploying and operationalising generative AI solutions, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows and real-time inference architectures in enterprise environments. Strong understanding of AI, machine learning operations and decisioning ecosystems. Experience working in multidisciplinary teams across analytics, technology and consulting. Track record of delivering commercial value through data and AI. Experience engaging senior stakeholders and translating technical concepts into business outcomes. Strong understanding of data engineering, cloud platforms and modern data architectures. Desirable
Experience with technologies and platforms such as: Databricks Snowflake SageMaker Pega Customer decisioning platforms Experience within one or more of the following sectors:
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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Kensington, London, UK
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