Applied AI Engineer

About the role

Who We Are:

At Bandwidth, we shape how the world connects. We're the global communications software company and Tier 1 network operator with global reach. The voice, messaging, and emergency services infrastructure behind the brands and apps you use every day.

Because we own the network we build on, the problems you'll solve here are bigger, more complex, and more impactful. And as communications shifts to AI, we're the infrastructure layer making it possible.

We call ourselves BANDmates because building something this big takes collaboration, different perspectives, and the freedom to bring your authentic self to the table.

We're experts who speak like humans. Ready to build what's next? #JoinTheBand

What We Are Looking For:

As an Applied AI Engineer on the Corporate IT Engineering team, you’ll identify where AI can create real leverage across our internal systems and operations, and build it. You’ll work inside a team that owns a broad and complex infrastructure footprint, embedding AI into the platforms and workflows that keep Bandwidth running. This is an infrastructure and systems role with a deep AI focus.

What You'll Do:

- Own and extend existing AI platforms and tooling, improving reliability, expanding capabilities, and integrating them more deeply with internal systems.

- Architect and build internal API layers and shared services that allow AI workflows and internal applications to publish, version, and retrieve outputs across the engineering ecosystem.

- Identify and build AI-powered tooling that creates leverage across the Corporate IT Engineering stack, including infrastructure, identity, monitoring, and automation platforms.

- Develop and iterate on proof-of-concepts that demonstrate how AI can augment or automate internal workflows; from anomaly detection in infrastructure logs to AI-assisted documentation and IT troubleshooting.

- Containerize and orchestrate AI workloads using Docker and Kubernetes, ensuring reliable and reproducible deployments across environments.

- Automate infrastructure provisioning and configuration using Terraform and Ansible, following infrastructure-as-code best practices.

- Establish AI development patterns and best practices across the Corporate IT Engineering organization, helping teams adopt AI capabilities effectively and responsibly.

- Stay current with the evolving AI and MLOps landscape and bring relevant advancements back to the team.

What You Need:

AI & Application Development

- Hands-on experience owning or extending LLM-powered platforms, including RAG pipeline development, prompt engineering, and integrating LLM APIs into production internal systems.

- Expert-level knowledge of AI infrastructure, including model serving, inference optimization, GPU/CPU resource management, and MLOps pipelines.

- Experience designing and building internal API layers or shared platform services that multiple teams and systems publish to and consume from.