ML Developer People Prime Worldwide
People Prime Worldwide
Office Location
Full Time
Experience: 3 - 3 years required
Pay:
Salary Information not included
Type: Full Time
Location: All India
Skills: azure devops, ACR, ARM, NumPy, Python, Docker, Kubernetes, Routing, load balancing, Code Commit, Azure Container services, Aks, Azure Resource Manager, Bicep, Azure Key Vault, Azure Machine Learning Studio, Kubeflow Serving, MLflow, Pandas, Scikitlearn, Tensorflow, CICD pipelines, Azure Infrastructure, Aiml, Azure Technologies, YAML scripting, networking concepts, VPCs, NACLs, private endpoints, VNet peering, CICD orchestration, Data science pipelines, Model Lifecycle Management, Drift Monitoring, Model retraining, Model technical evaluation, Business Validation, ML pipeline, Modelling infrastructure
About People Prime Worldwide
Job Description
As a Pipeline Construction Engineer, you will be responsible for building pipelines using Azure DevOps and Code Commit. Your expertise in Azure container services, including ACR and AKS, will be crucial for successful project implementation. Additionally, you will be utilizing Azure Resource Manager (ARM) and BICEP to manage resources efficiently and Azure Key Vault to ensure secure key management. Your role will also involve working with Azure Machine Learning studio and ML model serving technologies like Kubeflow Serving and MLflow. Proficiency in common data science frameworks such as Pandas, NumPy, Scikit-learn, and TensorFlow will be essential for developing robust machine learning models. You will be responsible for production deployments and developing end-to-end CI/CD pipelines. Experience in azure infrastructure and platform in the context of AI/ML will be beneficial. Your expertise in automations and deployments using cutting-edge Azure technologies will contribute to the efficiency of the process. Strong written and verbal communication skills are required for effective collaboration and listening to stakeholders. Knowledge of Azure Data Lake Storage and CI/CD processes on Azure DevOps will be necessary for seamless project execution. Understanding the key concepts of Infrastructure as Code (IaC) and practical experience in its application will be an advantage. Proficiency in Python and YAML scripting is essential for automating tasks and deployments. Experience with docker and container orchestration services like Kubernetes and AKS will be beneficial. Knowledge of common networking concepts such as VPCs, NACLs, Routing, load balancing, Private Endpoints, and VNET Peering is essential for effective infrastructure setup. You will be responsible for implementing CI/CD orchestration for data science pipelines and ensuring smooth production deployments. Your role will also involve post-deployment model lifecycle management activities such as drift monitoring, model retraining, and model technical evaluation for business validation. Collaborating with stakeholders to address technical issues related to ML pipelines and supporting modeling infrastructure needs will be a key aspect of your role. Your expertise and proactive approach will contribute to the successful implementation of AI/ML projects.,