Sr AI Platform Engineer- Hybrid in Alpharetta, GA

Interview integrity requirement
Candidates must personally complete all interviews and technical assessments. The use of proxies or third-party representatives during any stage of the hiring process is prohibited and will result in disqualification. Final candidates will be required to participate in at least one in-person interview. Some travel for this role is expected. Reasonable accommodations will be provided in accordance with applicable laws.

Build the Platform That Powers the Future of Engineering

We're looking for a Senior AI Platform Engineer to help design, build, and evolve the cloud platform that enables our engineering teams to deliver secure, scalable, and reliable software. This role combines cloud infrastructure, DevOps, automation, and modern platform engineering with an exciting opportunity to help shape how AI-powered development is adopted across our organization.
You'll work alongside software engineers, architects, and DevOps professionals to improve developer productivity, strengthen platform reliability, and build the capabilities that support AI-assisted and agentic engineering at scale.
If you're passionate about cloud technologies, infrastructure automation, developer experience, and leveraging AI to build better software, we'd love to talk with you.

What You'll Do

Design, build, and maintain scalable, secure, and highly available cloud platform infrastructure.
Develop Infrastructure as Code (IaC) using tools such as Terraform, CloudFormation, or Ansible.
Manage and optimize AWS, Azure, or Google Cloud environments for performance, reliability, scalability, and cost efficiency.
Build and enhance CI/CD pipelines that automate software delivery and infrastructure deployment.
Partner with software engineering teams to improve platform capabilities and developer experience.
Monitor platform health, troubleshoot production issues, and drive continuous operational improvements.
Implement security best practices across infrastructure, pipelines, and cloud environments.
Use AI-assisted development tools such as GitHub Copilot, Claude Code, or similar technologies to accelerate infrastructure development while validating quality, security, and maintainability.
Build and support the platform capabilities that enable engineering teams to safely adopt AI agents, including model access, MCP servers and gateways, tool integrations, identity management, and governance controls.
Extend observability and auditing capabilities to AI-driven workflows, ensuring agent actions are secure, traceable, and compliant.
Research emerging cloud, platform engineering, and AI technologies to continuously improve engineering practices.
Champion responsible AI adoption by establishing best practices, evaluating emerging tools, and helping engineering teams effectively integrate AI into their daily workflows.

What You Bring
Required Qualifications

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.
6+ years of experience in software, platform, DevOps, or cloud engineering.
Strong experience with one or more major cloud providers (AWS preferred, Azure or Google Cloud also considered).
Experience building Infrastructure as Code using Terraform, CloudFormation, or similar tools.
Experience with container technologies such as Docker and Kubernetes.
Proficiency with scripting or programming languages such as Python, Go, or Bash.
Hands-on experience building and maintaining CI/CD pipelines using tools such as Jenkins, GitLab CI, GitHub Actions, CircleCI, or similar.
Strong understanding of networking, cloud security, Linux systems, and platform operations.
Experience with observability and monitoring tools such as Datadog, Prometheus, Grafana, or similar.
Experience using AI-assisted development tools in a professional engineering environment and the ability to critically evaluate AI-generated code and infrastructure.

Preferred Qualifications

Experience designing or supporting AI platform capabilities and developer tooling.
Familiarity with large language models (LLMs), prompt engineering, agent orchestration, tool/function calling, and Model Context Protocol (MCP).
Understanding of governance, security, and operational considerations for deploying AI agents in production environments.

Additional Information
Occasional flexibility in work hours may be required to support production needs, cross-functional initiatives, and collaboration across multiple time zones.