Deqode
Website:
deqode.com
Job details:
Job Description
We are looking for an Azure MLOps Engineer who enjoys working at the intersection of machine learning, cloud infrastructure, and DevOps. In this role, you will be responsible for building, deploying, and maintaining scalable ML/AI solutions on Azure.
You will work closely with data scientists, data engineers, and application teams to take models from experimentation to production while ensuring reliability, scalability, and monitoring standards are in place.
This role is ideal for someone who is hands-on with Azure ML Studio, comfortable working with AKS deployments, and has a strong understanding of MLOps practices.
Key Responsibilities
- Build, deploy, and maintain machine learning and AI solutions on Microsoft Azure.
- Register, manage, and deploy ML/AI/GenAI models using Azure ML Studio.
- Design and implement end-to-end ML pipelines for training, inference, monitoring, and retraining.
- Work with Azure services such as AKS, Blob Storage, Azure Data Factory (ADF), and Azure DevOps pipelines.
- Deploy and manage models on AKS clusters for scalable production environments.
- Implement CI/CD pipelines and automate deployment workflows using Azure DevOps.
- Monitor model performance, drift, and operational health in production environments.
- Manage ML experiments, artifacts, and model tracking using MLflow.
- Collaborate with cross-functional teams to ensure smooth integration of ML solutions into business applications.
- Follow coding standards and best practices for Python development, including dependency management and code quality checks.
- Support troubleshooting, optimization, and continuous improvement of ML systems.
Required Skills & Experience
- Strong hands-on experience with :
- Azure ML Studio
- Azure Kubernetes Service (AKS)
- Azure Blob Storage
- Azure Data Factory (ADF)
- Azure DevOps Pipelines
- Experience deploying and managing ML/AI/GenAI models in Azure environments.
- Good understanding of MLOps concepts and production ML lifecycle.
- Experience with MLflow for experiment tracking and model management.
- Strong Python programming skills.
- Familiarity with virtual environments, dependency management, linting tools, and coding best practices.
- Experience working with Git and CI/CD workflows in Azure DevOps.
- Knowledge of model monitoring, drift detection, and performance tracking.
- Understanding of data engineering fundamentals and pipeline orchestration.
Good To Have
- Experience with Docker and containerized deployments.
- Exposure to GenAI or LLM-based applications.
Familiarity with scalable distributed systems and cloud-native architecture.
(ref:hirist.tech)
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