Senior AI Platform Engineer
Pennymac Financial Services INC - Carrollton, Texas; Carrollton, Texas
Posted Jan 6, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Senior AI Platform Engineer Carrollton, Texas; Carrollton, Texas PENNYMAC Pennymac (NYSE: PFSI) is a specialty financial services firm with a comprehensive mortgage platform and integrated business focused on the production and servicing of U.S. mortgage loans and the management of investments related to the U.S. mortgage market. At Pennymac, our people are the foundation of our success and at the heart of our dynamic work culture. Together, we work towards a unified goal of helping millions of Americans achieve aspirations of homeownership through the complete mortgage journey. A Typical Day The Senior AI Platform Engineer will: Design, implement, and manage scalable and resilient infrastructure on AWS. Architect and maintain Windows/Linux based environments, ensuring seamless integration with cloud platforms. Develop and maintain infrastructure-as-code(IaC) using both AWS Cloudformation/CDK and Terraform/OpenTofu. Develop and maintain Configuration Management for Windows & Linux servers using Chef. Design, build, and optimize CI/CD pipelines using GitLab CI/CD for .NET applications. Integrate and support AI services, including orchestration with AWS Bedrock, Google Agentspace, and other generative AI frameworks, ensuring they can be securely and efficiently consumed by platform services. Enable AI/ML workflows by building and optimizing infrastructure pipelines that support large-scale model training, inference, and deployment across AWS and GCP environments. Automate model lifecycle management (training, deployment, monitoring) through CI/CD pipelines, ensuring reproducibility and seamless integration with development workflows. Collaborate with AI engineering teams to deliver scalable environments, standardized APIs, and infrastructure that accelerate AI adoption at the platform level. Implement observability, security, data privacy and cost-optimization strategies specifically
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Apply link verified; last checked Jun 13, 2026.
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