Machine Learning Engineer

Inara

Remote (Anywhere)

Accepting Applications Full-time Remote
Posted 1 week ago 1 views 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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