AI Platform Engineering for Scalable Model Deployment on Azure

AI Platform Engineering for Scalable Model Deployment on Azure

Client Overview:
We are a mid-sized AI-driven SaaS company focused on delivering data intelligence solutions across finance and healthcare. We're expanding our cloud infrastructure and looking for an experienced AI Platform Engineer to help productionize machine learning models using Azure and Python.

Project Duration:
3–6 months (with potential for extension)

Estimated Workload:
20–30 hours/week (flexible)

Location:
Remote – global applicants welcome
(Overlap with Europe/US working hours preferred)

Project Scope:

You’ll be working with our Data Science and DevOps teams to design, develop, and deploy a scalable MLOps framework on Azure. The key objective is to enable seamless end-to-end model deployment, monitoring, and lifecycle management for several AI models currently in experimentation phase.
Responsibilities:

Design and implement a production-ready AI/ML pipeline using Azure services (Azure ML, Azure Functions, AKS, etc.)

Package and deploy Python-based ML models using Docker and Azure Kubernetes Services (AKS)

Automate workflows using Azure Pipelines / GitHub Actions

Implement CI/CD for model training, validation, and deployment

Ensure logging, monitoring, and rollback capabilities (using Application Insights, MLflow, or custom solutions)

Collaborate with Data Scientists to translate notebooks into production services

Optimize platform for cost, scalability, and performance

Required Skills:

Strong proficiency in Python for scripting, automation, and ML pipelines

Deep experience with Azure AI/ML ecosystem (Azure ML, AKS, Blob Storage, Azure Pipelines)

Hands-on experience with Docker and Kubernetes in a production setting

Familiarity with MLflow, FastAPI, TensorFlow/PyTorch/Scikit-learn model formats

Experience building CI/CD pipelines for ML workflows

Strong understanding of MLOps and cloud security best practices

Preferred Skills (Nice to Have):

Experience integrating with DataBricks, Power BI, or other Azure analytics tools

Familiarity with Terraform or Bicep for infrastructure-as-code (IaC)

Experience with hybrid cloud/on-premise model deployments

Deliverables:

Working Azure-based ML pipeline supporting one or more models

Documentation (pipeline design, deployment instructions, monitoring guide)

Automated retraining and rollback mechanism

CI/CD setup in GitHub or Azure DevOps

Budget:

$50–$100/hour (depending on experience)

Application Deadline:
August 25, 2025

To Apply:
Please include:

• A brief summary of relevant experience (especially Azure MLOps projects)
• A link to your portfolio, GitHub, or sample project code
• Availability (start date and weekly hours)
• Hourly rate