Accepting Applications
Full-time
Remote
Posted 1 week ago
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0 applications
Job Description
**Contract Machine Learning Engineer**
**Duration:**
Initially 3 months
**Day rate:**
£500, 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 ML 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)**
* Python (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)
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