Google Machine Learning Engineer

Skills

Description

This role Cloud Machine Learning Engineer requires in-depth experience in design, build, automate, deploy & monitor of ML pipelines on Azure. The candidate would have extensive experience with Azure ML Studio, building & operationalizing Kubeflow pipelines, Data Labelling, Supervised & unsupervised models, Generative AI, LLM with both real-time and batch applications. The candidate is expected to guide and lead Wells Fargo’s Google Cloud adoption and migration by building the appropriate foundational blocks including building the MLOPS pipeline framework, continuous training and model monitoring. The candidate would help document the Azure Cloud deployment Guidelines and Playbook and help pilot model deployment on Production Scoring environment using the pipeline designed and by following all Wells Fargo’s Release engineering process.

Responsibilities

• • 2+ years of Azure Cloud experience using Azure ML Studio, Building and operationalizing ML pipelines using sound MLOPS practices.
• • 2+ years of Experience with ML frameworks and libraries, such as XGBoost, Keras, Pytorch, scikit etc
• • 1+ years of Experience with 3 or more technologies like : LLM, Generative AI, Vector DB, Snorkel, Feature Store, Data Labeling
• • 1+ years Terraform OR 2+ years Infrastructure as Code (Puppet, Chef, Ansible, homebrew, etc. are acceptable in lieu of Terraform) -- This is for Terraform consultant
• • 5+ years of development experience with languages such as Python, Java, Scala, or R
• • 3+ years of experience with scripting languages such as Bash, PowerShell, Python, Shell, VBScript, or JavaScript
• • 2+ years of experience developing and using RESTful Application Program Interface (API)
• • Hands-on experience migrating workloads to public cloud (Either GCP or Azure)