Build and operationalize scalable AI solutions by embedding machine learning pipelines across cloud environments.

Collaborate with Data Scientists, Analysts, and peer AI Engineers to deliver advanced analytics algorithms and applications via proof of concepts and experiments
Prepare data models, implement data quality checks, and optimize analytical solutions for large-scale execution
Design and develop applications that pull data from multiple systems using Python and SQL
Support AI and analytics solutions in multi-cloud setups:
- Azure (Databricks, Azure ML, AKS, ADF)
- GCP (Composer, BigQuery, GKE, Kubeflow)
Operate with DevOps and Agile practices (Jira), using CI/CD through GitHub Actions to improve code reusability and design patterns from internal libraries
Operationalize Machine Learning models as core components of IT solutions and business processes
Apply Platform Engineering practices to reduce friction for data scientists and model developers by making cloud infrastructure “invisible”
Troubleshoot infrastructure issues to keep critical AI products running
Build proactive incident detection using multiple datasets and real-time data stream analyses
Create and deliver prototypes or proofs of concept for new features; suggest and implement architecture improvements
Strengthen DevOps practices within AI Engineering by coaching and teaching others

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field
2+ years of proven experience with Python and SQL, including data modeling, data and analytics product design, development, and deployment
Ability to work a hybrid schedule in Cincinnati, OH based office

Python, SQL
Google Cloud Platform (BigQuery, Kubeflow, GKE, Composer)
Azure (Databricks, Azure ML, AKS, ADF)
DevOps, Agile, Jira, CI/CD, GitHub Actions
Machine Learning, Artificial Intelligence
PyTest, Sonarqube, GitHub
Load balancing, autoscaling, firewalls, authentication, Logging, monitoring, real-time data stream analyses

Experience with Agile, CI/CD, and DevOps methodologies, including tools such as Jira and GitHub Actions
Testing experience (e.g., PyTest) including mocking; static code analysis (e.g., Sonarqube); source control management with GitHub
Knowledge of GCP/Azure infrastructure concepts such as load balancing, autoscaling, and authentication, including security topics like firewalls
Experience delivering AI/ML products with model pipelines and deployment processes
Experience with logging and monitoring technologies
Strong written and verbal communication in English
Problem-solving skills in complex cloud environments

USD 85,000 - 122,200 per year
Total rewards may include salary + bonus (if applicable) + benefits
Compensation varies by office location, degree/credentials, relevant skills, and experience

AI Engineer

Job Description