Posted:1 day ago|
Platform:
Remote
Full Time
We are seeking a MLOps Engineer to design, implement, and manage scalable machine learning infrastructure and automation pipelines. The ideal candidate will have deep hands-on expertise in Azure, AKS, Infrastructure as Code, and CI/CD, with a passion for enabling efficient and reliable deployment of machine learning models in production environments. Responsibilities:- Architect & Deploy: Design and manage scalable ML infrastructure on Azure (AKS), leveraging Infrastructure as Code principles. Automate & Accelerate: Build and optimize CI/CD pipelines with GitHub Actions for seamless software, data, and model delivery. Engineer Performance: Develop efficient and reliable data pipelines using Python and distributed computing frameworks. Ensure Reliability : Implement solutions for deploying and maintaining ML models in production. Collaborate & Innovate : Partner with data scientists and engineers to continuously enhance existing MLOps capabilities. Expertise:- Azure & AKS : Deep hands-on experience. IaC & CI/CD : Mastery of Terraform/Bicep & GitHub Actions. Data Engineering : Advanced Python & Spark for complex pipelines. ML Operations : Proven ability in model serving & monitoring. Problem Solver: Adept at navigating complex technical challenges and delivering solutions. Nice to Have:- Experience with Kubeflow, MLflow, or similar MLOps tools. Exposure to other cloud platforms (AWS, GCP) in addition to Azure. Familiarity with security, compliance, and cost-optimization for ML workloads.
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