This role is for a Machine Learning Engineer with a contract length of "unknown" and a pay rate of £500-£560. Key skills include Python, ML frameworks (PyTorch, TensorFlow), and experience with AWS or GCP. Strong commercial experience in ML pipelines and asynchronous inference is required.United Kingdom560March 21, 2026----London, England, United Kingdom#Deployment #Airflow #Scala #AWS (Amazon Web Services) #GCP (Google Cloud Platform) #MLflow #Spark (Apache Spark) #SageMaker #Monitoring #Python #TensorFlow #Batch #PyTorch #ML (Machine Learning) #Model Deployment #Databricks #Cloud #Observability #Kafka (Apache Kafka)ML Engineer£500-£560The CompanyThey are a well‑established consumer technology business with a mission to create a safe, trusted environment for millions of users. Data, experimentation, and scalable engineering are core to how they operate. Their Trust function is expanding, and they are investing heavily in modern ML infrastructure to support rapid growth. You will join a collaborative environment where ML Engineers, Scientists, Backend Engineers, and MLOps specialists work closely to deliver measurable impact.The RoleIn this role, you will:• Design and implement pipelines for training, evaluating, deploying, and monitoring detection models• Productionise detection models in partnership with ML Scientists, improving reliability and latency across asynchronous inference workflows• Collaborate with backend and product teams to define integration requirements for trust detection services• Extend ML infrastructure with MLOps teams, including reproducible training workflows, CI/CD for model deployment, batch and real‑time model serving, feature consistency, and monitoring• Uphold strong standards around testing, observability, and operational excellence• Contribute to a scaling engineering culture where experimentation and measurable outcomes are centralYour Skills and Experience• Strong commercial experience building and deploying ML pipelines in production• Experience with asynchronous ML inference pipelines• Deep understanding of end‑to‑end ML workflows from research through to deployment• Ability to operate with ownership in complex, fast‑moving environments• Strong communication skills for working with both technical and non‑technical stakeholders• Experience designing systems within modern cloud environments such as AWS or GCP• Proficiency in Python and common ML frameworks such as PyTorch, TensorFlow, or scikit‑learn• Exposure to ML/MLOps tooling such as SageMaker, MLflow, or TFServing• Experience with Spark, Databricks, CI/CD tools, and streaming or orchestration systems like Kafka or AirflowHow to ApplyIf you are interested in this Machine Learning Engineer position, please apply with your CV.Freelance data hiring powered by an engaged, trusted community — not a CV database. #J-18808-Ljbffr