Machine Learning Engineer

Inara
Nottingham
2 months ago
Applications closed

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Senior Machine Learning Engineer

Contract Machine Learning Engineer | MLflow | Databricks | Production ML


Duration: Initially 3 months

Day rate: £500 - £550, Inside IR35

Workplace: Remote, with occasional travel to client-site


Inara are supporting a consultancy-led team delivering production-grade machine learning platforms for a range of end clients, and they’re looking for a senior, hands-on Contract MLOps Engineer to help take ML systems from experimentation into reliable, scalable production.


This role is firmly focused on ML enablement and platform engineering rather than model research. You’ll be the person ensuring models can be trained, tracked, deployed, governed, and monitored properly in real-world environments.


What you’ll be doing

  • Designing and building end-to-end MLOps platforms that support the full ML lifecycle
  • Implementing and operating MLflow for experiment tracking, model registry, and versioning
  • Enabling production deployments of ML models (batch and/or real-time)
  • Putting robust CI/CD pipelines in place for ML workflows
  • Partnering closely with Data Scientists to move models from notebooks into production
  • Establishing best practices around model governance, monitoring, retraining, and environments
  • Integrating ML platforms with Databricks and cloud-native services


What we’re looking for

  • Strong, real-world MLOps experience (this is not a theoretical role)
  • Deep hands-on MLflow experience — this is essential
  • Proven track record of productionising ML models across multiple client or project environments
  • Background in one or more of:
  • MLOps / ML Engineering
  • DevOps with ML platforms
  • Data Science with a strong production focus
  • Experience designing, supporting, and operating ML systems in production


Technical environment (experience expected across most of these)

  • MLflow (expert-level)
  • Databricks
  • Cloud platforms (AWS preferred; SageMaker exposure a bonus)
  • CI/CD for ML workloads
  • Docker and Kubernetes
  • Infrastructure as Code (Terraform or similar)
  • Python-based ML workflows

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