AI Platform Engineer

Strategic Staffing Solutions; Detroit (MI); On-site; USD 130,000 - 180,000; Full time

Job summary

Strategic Staffing Solutions seeks a Senior AI Platform Engineer to join our Advanced Analytics team in Detroit. The role focuses on turning AI/GenAI prototypes into secure, scalable production-ready applications on Azure, spanning data engineering, DevOps, and platform engineering.

You will own data ingestion pipelines, REST API integrations, and CI/CD processes, collaborating with data scientists and stakeholders to productionize AI solutions in a real-world enterprise environment.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 5+ years of software, data, AI/ML, cloud, or platform engineering experience.
• Strong Python and SQL skills with production-grade applications.

Responsibilities

• Productionize AI/ML and GenAI solutions within Azure-based environments.
• Design and develop production-quality Python applications, APIs, services.
• Build data ingestion and ETL/ELT pipelines for analytics and AI use cases.
• Integrate AI solutions with enterprise data sources and APIs.
• Own DevOps, MLOps, CI/CD, monitoring, and operational support for AI apps.
• Deploy and support applications on Microsoft Azure.

Skills

Python

SQL

Azure

DevOps

MLOps

Data pipelines

REST APIs

CI/CD

GitHub Actions

Education

Bachelor's degree in Computer Science

Tools

Docker

GitHub Actions

Podman

Job description

We have an exciting multi year contract available with our energy customer in Detroit!

2 year+ contract

W2 only, NO CORP TO CORP. We can transfer your H1 but you must work on our W2.

Highly competitive rate!

About the Role

We are seeking a Senior AI Platform Engineer to join our Advanced Analytics team and help transform analytical, machine learning, and Generative AI solutions into secure, scalable, production-ready applications.

Data Scientists and Analytics professionals focus on models, GenAI applications, RAG solutions, experimentation, and analytical approaches. This role complements those capabilities by owning software engineering, cloud architecture, data integration, system integration, DevOps, MLOps, CI/CD, and productionization.

This is a hands-on engineering role at the intersection of Python development, data engineering, Azure cloud engineering, AI/ML solutions, and DevOps/MLOps. The ideal candidate can take a solution developed in a local Linux environment and help turn it into a reliable, secure, maintainable application running in Azure.

Key Responsibilities

AI & Analytics Solution Engineering

• 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.
• Partner with centralized data engineering teams when enterprise data landing or platform capabilities are required, while owning project-level data integration and processing needs.
• Design, deploy, and support AI and analytics applications within Microsoft Azure.

Minimum Qualifications

• 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.
• Experience productionizing Machine Learning, Generative AI, LLM, RAG, or agent-based applications.