ML/Ops Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an ML/Ops Engineer on a contract basis, lasting up to £500 per day, fully remote. Required skills include ML Ops tools, cloud infrastructure, CI/CD practices, and compliance in regulated environments.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
500
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🗓️ - Date discovered
September 26, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Remote
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📄 - Contract type
Outside IR35
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United Kingdom
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🧠 - Skills detailed
#Deployment #Security #GCP (Google Cloud Platform) #SageMaker #DevOps #AWS (Amazon Web Services) #Compliance #ML (Machine Learning) #Kubernetes #MLflow #Cloud #Monitoring #Azure #Version Control #Docker #ML Ops (Machine Learning Operations) #Scala #AI (Artificial Intelligence) #Data Science #Logging
Role description
OUTSIDE IR35 + REMOTE – UP TOO £500 P/D Inspirec has partnered with a dynamic and innovative leader in the technology industry, who are seeking a highlymotivated AI/ML DevOps Engineer to join their team on a contract basis. We are seeking an experienced ML Ops / DevOps Engineer to manage the deployment, monitoring, and lifecycle of AI/ML solutions. This role ensures the reliability, security, and auditability of machine learning models in production environments, supporting scalable and compliant AI operations. Key Responsibilities: • Design, build, and maintain robust deployment pipelines for machine learning models. • Monitor model performance and data drift; manage retraining and redeployment processes. • Ensure version control, reproducibility, and auditability of all AI/ML assets. • Implement effective logging, monitoring, and alerting for AI services and infrastructure. • Guarantee adherence to security, governance, and infrastructure standards, especially in regulated environments. • Collaborate closely with data scientists and software engineers to productionize prototypes into scalable, maintainable solutions. Essential Skills & Experienc: • Hands-on experience with ML Ops tools such as MLflow, Kubeflow, Amazon SageMaker, Vertex AI, orequivalent platforms. • Deep understanding of cloud infrastructure services (AWS, Azure, GCP). • Strong experience with CI/CD practices and containerization tools (Docker, Kubernetes). • Knowledge of the machine learning model lifecycle, including drift detection and automated retraining. • Familiarity with information security best practices and compliance standards, particularly in government or highly regulated contexts.