Build and operationalize AI with Procter & Gamble in a hybrid role based in Cincinnati, OH. You’ll embed machine learning pipelines on Google Cloud Platform, work across multi-cloud services, and help production teams scale analytics and AI capabilities through reliable deployments, testing, and platform engineering.
Compensation: USD 85,000 - 122,200 per year. P&G total rewards include salary + bonus (if applicable) + benefits, with final range and details depending on role, location, credentials, skills, and experience.
Immigration: Immigration Sponsorship is not available for this role. P&G participates in E-Verify as required by law.
What you’ll do
- Collaborate with Data Scientists, Analysts, peer AI Engineers to deliver advanced analytics solutions through proof of concepts and experiments.
- Prepare data models, establish quality checks, and optimize analytical solutions for scale.
- Design and build applications that pull data from multiple systems, with development in Python and SQL.
- Support solutions across Azure (Databricks, Azure ML, AKS, ADF) and/or GCP (Composer, BigQuery, GKE, Kubeflow).
- Operate with DevOps practices and Agile workflows (Jira), using CI/CD via GitHub Actions to improve code reuse and design patterns from internal libraries.
- Operationalize Machine Learning models as an integral part of IT solutions and business processes.
- Apply Platform Engineering practices so cloud infrastructure is effectively “invisible” to data scientists and model developers.
- Troubleshoot infrastructure issues to keep critical AI products running, including incident detection driven by multiple datasets and real-time data stream analysis.
- Develop prototypes or proof of concepts for new features, propose architecture improvements, and strengthen AI Engineering DevOps practices through coaching or teaching others.
Required qualifications
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
- 2+ years of proven experience with Python & SQL, including data modeling, and development and deployment of Data and Analytics products.
- Ability to work a hybrid schedule in Cincinnati, OH.
Preferred experience
- Experience with Agile, CI/CD, and DevOps methodologies, including tools such as Jira and GitHub Actions.
- Testing and engineering practices such as PyTest and mocking, plus static code analysis (e.g., Sonarqube) and source control using GitHub.
- Cloud infrastructure knowledge for GCP and/or Azure, including load balancing, autoscaling, firewalls, and authentication.
- Experience with AI/ML products, model pipelines, and deployment processes.
- Logging and monitoring technologies.
- Strong written and verbal communication in English.
- Problem solving skills in complex cloud environments.
Technologies: Python, SQL, Google Cloud Platform, BigQuery, Kubeflow, Azure, Databricks, Azure ML, AKS, ADF, Composer, GKE, 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.
