Role Overview – Deqode is hiring an Azure ML / MLOps Engineer for a fully remote, work-from-home position based in India. This position is ideal for technically proficient machine learning engineers with strong Azure ML expertise, MLOps capabilities, and the ability to build, deploy, and operationalize machine learning models, implement ML pipelines, and manage the complete ML lifecycle within Azure cloud environments.

Azure Machine Learning Platform Management – You will manage and optimize Azure Machine Learning platforms by configuring Azure ML workspaces, managing compute resources, setting up training and inference clusters, implementing Azure ML pipelines, and ensuring efficient, scalable ML infrastructure that supports model development, training, and deployment.

MLOps Pipeline Development – You will develop and implement MLOps pipelines by building automated ML workflows, creating CI/CD pipelines for machine learning models, implementing continuous training and retraining pipelines, automating model deployment processes, and establishing end-to-end MLOps practices that accelerate model delivery and improve reliability.

Model Deployment and Serving – You will deploy and serve machine learning models by containerizing models using Docker, deploying models to Azure Kubernetes Service (AKS), Azure Container Instances (ACI), or Azure ML endpoints, implementing real-time and batch inference services, managing model versioning, and ensuring high-availability model serving infrastructure.

Model Monitoring and Performance Tracking – You will monitor models and track performance by implementing model monitoring solutions, tracking model drift and data drift, setting up performance metrics and alerts, analyzing model behavior in production, implementing logging and observability, and ensuring models maintain accuracy and reliability over time.

Infrastructure as Code and Automation – You will implement infrastructure as code and automation by using Terraform, ARM templates, or Bicep for Azure resource provisioning, automating environment setup, implementing configuration management, creating reusable infrastructure templates, and ensuring consistent, reproducible ML infrastructure deployment.

Data Pipeline Development and Management – You will develop and manage data pipelines by building data ingestion workflows, implementing ETL/ELT processes, coordinating with Azure Data Factory, Azure Databricks, or Azure Synapse Analytics, managing data transformation pipelines, and ensuring reliable data flows that support model training and inference.

Version Control and Experiment Tracking – You will implement version control and experiment tracking by using MLflow, Azure ML experiments, or similar tools to track model experiments, managing model registries, versioning datasets and models, documenting model lineage, and ensuring reproducibility of ML experiments and deployments.

Model Governance and Compliance – You will establish model governance and ensure compliance by implementing model approval workflows, maintaining model documentation, establishing model validation processes, ensuring regulatory compliance, implementing audit trails, and maintaining governance standards for production ML systems.

Azure Cloud Services Integration – You will integrate Azure cloud services by leveraging Azure Storage, Azure Key Vault for secrets management, Azure Monitor and Application Insights for observability, Azure DevOps or GitHub Actions for CI/CD, and other Azure services to build comprehensive ML solutions.

CI/CD and DevOps Implementation – You will implement CI/CD and DevOps practices by building automated testing frameworks for ML models, implementing continuous integration pipelines, automating deployment workflows, managing release processes, and ensuring reliable, efficient model delivery to production.

Collaboration with Data Scientists and Engineers – You will collaborate with data scientists and engineers by supporting model development workflows, providing MLOps tooling and platforms, assisting with model optimization and deployment, bridging the gap between research and production, and enabling data science teams to deliver models efficiently.

Performance Optimization and Scaling – You will optimize performance and implement scaling by tuning model inference latency, optimizing compute resources, implementing auto-scaling for inference endpoints, managing cost optimization, and ensuring ML systems can handle production workloads efficiently.

Security and Access Management – You will implement security and access management by configuring Azure Active Directory integration, managing role-based access control (RBAC), implementing network security, encrypting data at rest and in transit, managing secrets and credentials securely, and ensuring ML systems meet security standards.

Containerization and Orchestration – You will implement containerization and orchestration by creating Docker containers for models and applications, managing Kubernetes deployments, implementing Helm charts, orchestrating microservices architectures, and ensuring scalable, resilient ML service deployments.

How to Apply:

Send your updated CV to shdas@deqode.com

Please highlight your Azure ML and MLOps experience, examples of ML pipelines and deployment systems you have built, your proficiency with Azure services and MLOps tools, your Python and containerization skills, and links to your GitHub profile or portfolio if available. Forward to anyone in your network who may be a strong fit.

About the Company:

Deqode is seeking an experienced Azure ML / MLOps Engineer who brings strong Azure Machine Learning expertise, MLOps capabilities, and proven experience building and operationalizing machine learning systems to develop scalable ML infrastructure, implement automated ML pipelines, and drive ML excellence within a fully remote and collaborative environment.