Senior AI Platform Engineer — Productionize AI on Azure

Strategic Staffing Solutions

Detroit (MI)

Hybrid

USD 96,000 - 152,000

Full time

14 days+

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Job summary

Strategic Staffing Solutions in Detroit, MI is seeking a Senior AI Platform Engineer to join our Advanced Analytics team on a W2 12-month contract. This role is hybrid, with on-site attendance Tue-Thu.

You will productionize AI/ML and Generative AI solutions, build scalable Python services, and design data pipelines and ETL/ELT processes to support analytics and enterprise applications on Azure. Ideal candidates have strong Python/SQL, experience with DevOps/MLOps, REST APIs, and deploying in

Qualifications

Bachelor's degree or equivalent practical experience in a technical field.
5+ years of experience in software, data, AI/ML, cloud, or platform engineering.
Strong Python and SQL skills with production-grade app experience.
Experience building data ingestion, ETL/ELT pipelines for analytics/AI.
DevOps and MLOps experience including CI/CD and GitHub Actions.
REST API development and enterprise application integration.
Azure deployment experience and containerized apps (Docker/Podman).
Linux administration, troubleshooting, and shell scripting.

Responsibilities

Partner with Data Scientists and stakeholders to productionize AI/ML and Generative AI solutions.
Translate prototypes into scalable, maintainable software applications.
Develop production-quality Python apps, APIs, services, and integration components.
Design integrations between AI solutions and enterprise data sources and systems.
Establish reusable engineering patterns for AI/analytics solutions.
Build and maintain data ingestion and integration pipelines for analytics/AI.
Develop and maintain ETL/ELT processes for data preparation.
Integrate data from APIs, databases, files, and apps.
Design reliable data workflows for applications and analytics.
Design, deploy, and support AI apps in Microsoft Azure.
Work with Azure resources (Functions, App Service, Container Apps, Storage, Key Vault, AI services, Monitor).
Understand Azure identity, authentication, authorization, networking and security.
Work in Linux environments and demonstrate system administration skills.
Troubleshoot issues across local and cloud environments.
Design and implement CI/CD pipelines via GitHub Actions.
Automate movement of apps from development to test/production.
Apply MLOps across ML/AI lifecycle.