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As an ML Ops Engineer on the Machine Learning team, you’ll collaborate with Data Scientists, Computer Vision/Machine Learning Engineers, Data Engineers, and members across Software Engineering, Product, and Sales teams to enable the development of robust, scalable machine learning models for identification and annotation. We need an engineer that can bring together practices from DevOps, Data Engineering, Machine Learning Engineering, and Data Science Research to build on and improve the ML Ops process by removing friction, increasing efficiency and making model development and deployment easier and faster.
 
Our client leverages all available tools and technologies to build a best-in-class tech-stack, which affords then flexibility of fast-deployments, along with the stability to support aggressive SLAs for critical-path client APIs and applications.
Seeking professional ML Ops Engineer with solid technical background, excelling in understanding complex problems and processes and proactively proposing efficient solutions.
 

Skills

    • BS in Computer Science or other quantitative discipline. 
    • 5-8 years of total experience with 3+ years of that experience working with tools and solutions for the Machine Learning lifecycle (build, deploy, and production support), ML pipelines and popular ML Ops tooling. 
    • Some experience with Kubernetes clusters, preferably on Amazon EKS. Familiarity with DevOps and CI/CD (Github Actions, Jenkins, other equivalent tools). 
    • Knowledge of ML Ops elements and architectures (data-ops, feature stores, artifacts and metrics tracking). 
    • Significant programming experience in Python programming and other scripting languages including relevant libraries and best practices (e.g.,  unit tests, CI/CD). 
    • Solid knowledge of containers and orchestration tools (e.g., Docker, K8s). 
    • Broad understanding of the process of development of an ML solution. 
    • Familiarity with the Linux environment including shell scripting and with Git. 
    • Experience with cloud platforms (e.g., AWS, GCP
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