Ubique Systems
Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer in London, UK, on a 5-month Inside IR35 contract. Key skills include Kafka, Flink, AWS SageMaker, and PyTorch. Experience with real-time data architectures and cloud environments is required. Hybrid work: 2 days in-office weekly.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
January 31, 2026
🕒 - Duration
3 to 6 months
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🏝️ - Location
Hybrid
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📄 - Contract
Inside IR35
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🔒 - Security
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#Storage #Cloud #ML (Machine Learning) #PyTorch #S3 (Amazon Simple Storage Service) #AWS S3 (Amazon Simple Storage Service) #Microservices #AWS (Amazon Web Services) #Kafka (Apache Kafka) #Model Deployment #Data Pipeline #Deployment #Batch #Redis #SageMaker #Data Storage #MongoDB #AWS SageMaker #Data Architecture
Role description
Job Title: ML Engineer
Location: London, UK - Hybrid: 2 Days to Office Every Week
Duration: 5 Months
Employment Type: Inside IR35 Contract
Roles & Responsibilities:
We’re looking for an experienced ML Engineer to design and operate real-time data and ML pipelines in a cloud-native environment. You’ll work on streaming, model training, and production deployment at scale.
Key responsibilities
• Build and manage real-time streaming pipelines (Kafka / Flink)
• Implement micro-batch processing (5-minute, hourly, daily)
• Design data pipelines using AWS S3
• Set up and manage Redis clusters
• Develop, train, and deploy ML models using AWS SageMaker
• Implement MLOps pipelines for training and model deployment
• Build and optimize models using PyTorch
• Evaluate data storage approaches (S3 vs MongoDB Atlas)
Required skills
• Strong experience with Kafka and Flink
• Hands-on AWS SageMaker (training, deployment, MLOps)
• Solid PyTorch experience
• Experience with real-time / streaming data architectures
• Strong cloud and microservices background
Job Title: ML Engineer
Location: London, UK - Hybrid: 2 Days to Office Every Week
Duration: 5 Months
Employment Type: Inside IR35 Contract
Roles & Responsibilities:
We’re looking for an experienced ML Engineer to design and operate real-time data and ML pipelines in a cloud-native environment. You’ll work on streaming, model training, and production deployment at scale.
Key responsibilities
• Build and manage real-time streaming pipelines (Kafka / Flink)
• Implement micro-batch processing (5-minute, hourly, daily)
• Design data pipelines using AWS S3
• Set up and manage Redis clusters
• Develop, train, and deploy ML models using AWS SageMaker
• Implement MLOps pipelines for training and model deployment
• Build and optimize models using PyTorch
• Evaluate data storage approaches (S3 vs MongoDB Atlas)
Required skills
• Strong experience with Kafka and Flink
• Hands-on AWS SageMaker (training, deployment, MLOps)
• Solid PyTorch experience
• Experience with real-time / streaming data architectures
• Strong cloud and microservices background
