AI Platform Engineer

Strategic Staffing Solutions

Detroit, MI, United States

1 day ago

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Role details

Contract type

Temporary contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon S3 Business Analytics Applications Data Analysis User Authentication Microsoft Azure Command-Line Interface Cloud Computing Information Systems Databases Continuous Integration

+30 more

Extract Transform Load (ETL) Data Systems Linux DevOps Github Python (Programming Language) Key Management Linux System Administration Machine Learning Package Management Systems Software Maintenance Standard Sql Azure Machine Learning Search Technologies Shell Script Software Deployment Systems Integration Enterprise Software Applications Data Ingestion Generative AI Fastapi Containerization Information Technology Deployment Automation Machine Learning Operations Restful APIs Data Pipelines Serverless Computing Azure Resource Manager Docker

Job description

Partner with Data Scientists, Analytics professionals, and business stakeholders to productionize AI, ML, and Generative AI solutions.
Translate analytical and AI prototypes into scalable, maintainable software applications.
Develop production-quality Python applications, APIs, services, and integration components.
Design integrations between AI solutions and enterprise applications, data sources, APIs, and downstream systems.
Establish reusable engineering patterns for AI and analytics solutions.
Build and maintain data ingestion and integration pipelines supporting analytics and AI applications.
Develop and maintain ETL/ELT processes to acquire, transform, validate, and prepare data for analytical and AI use cases.
Integrate data from APIs, databases, files, enterprise applications, and other source systems.
Design reliable, maintainable data workflows appropriate for application and analytical requirements.
Design, deploy, and support AI and analytics applications within Microsoft Azure.
Work with Azure Functions, Azure App Service, Azure Container Apps, Azure Storage, Azure Key Vault, Azure AI services, Azure AI Foundry, Azure AI Search, Azure Monitor, and related Azure resources.
Understand Azure identity, authentication, authorization, networking, security, and resource-management concepts.
Work comfortably in Linux-based development and runtime environments.
Demonstrate practical Linux system administration knowledge, including processes, services, permissions, networking, package management, shell scripting, logs, and system troubleshooting.
Troubleshoot application and environment issues across local Linux and cloud-hosted environments.
Design and implement CI/CD pipelines using GitHub Actions to automate testing, building, and deployment.
Automate movement of applications from development through test and production environments.
Apply MLOps practices across the lifecycle of machine learning and AI applications from development through production.

Requirements

Bachelor s degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
5+ years of experience in software, data, AI/ML, cloud, or platform engineering.
Strong Python and SQL skills, with experience building production-grade applications and data solutions.
Hands-on experience designing and building data ingestion, ETL/ELT pipelines, and scalable data engineering solutions to support analytics, AI/ML, and enterprise applications.
Hands-on experience with DevOps and MLOps, including CI/CD, GitHub Actions, deployment automation, monitoring, and operational support.
Experience developing and integrating REST APIs and enterprise applications.
Hands-on experience deploying applications and services on Microsoft Azure.
Experience with Docker, Podman, or similar container technologies.
Strong Linux administration, troubleshooting, and command-line experience.

*Beware of scams. S3 never asks for money during its onboarding process