MLOps Engineer - DevOps and Machine Learning

Oliver Bernard United Kingdom
Remote
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AI Summary

Design, build, and support Machine Learning platforms for egaming, trading, and payments projects. Requires strong DevOps and MLOps experience, knowledge of cloud platforms, and coding skills.

Key Highlights
Design and deployment of high-quality, scalable platforms
Define overall strategy and roadmap for ML and AI
Support platform deployments and CI/CD tools and processes
Key Responsibilities
Design and build Machine Learning platforms
Support platform deployments and CI/CD tools and processes
Define overall strategy and roadmap for ML and AI
Technical Skills Required
Machine Learning MLOps Cloud (AWS, Azure, GCP, OpenStack) DevOps Kubernetes IaC CI/CD Coding/scripting (Python, Bash) Linux Spark MLlib Tensorflow PyTorn REST FastAPI Docker
Benefits & Perks
£100K annual salary

Job Description


DevOps Engineer - MLOps - £100K


Our client is a leading tech consultancy and software development firm.


Offering fully remote working, working on a range of egaming, trading and payments projects they’re looking to hire an MLOps Engineer with great DevOps / Platform knowledge


You’ll work in their Data Platforms team and be key in the design, building and support of Machine Learning platforms to deliver the power and scale of AI capabilities.


Work will include the design and deployment of high quality, scalable platforms for running production Machine Learning software, helping define the overall strategy and roadmap for ML and AI and supporting platform deployments and CI/CD tools and processes.


You’ll need a positive, growth, mindset, great experience of ML frameworks and software delivery and good knowledge of DevOps, MLOps and Security.


Requirements:


  • Proven Machine Learning and MLOps experience
  • Good knowledge of Cloud - AWS, Azure, GCP, OpenStack etc
  • Strong DevOps experience - Kubernetes, IaC, CI/CD etc
  • Coding / scripting skills
  • Good, overall, understanding of software development, modern tech and frameworks
  • Solid Linux knowledge can't hurt!


Tech / Tools you’ll be working with (some of them!)


  • Kubeflow
  • Spark / MLlib / Tensorflow / PyTorn
  • REST / FastAPI
  • Docker / Kubernetes
  • Terraform / Ansible
  • GitOps
  • Python / Bash

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