<p><strong>Who We Are:</strong></p> <div> <p>At <strong>Bandwidth</strong>, 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.</p> <p>Because we own the network we build on,<em> the problems you'll solve here are bigger, more complex, and more impactful. </em>And as communications shifts to AI, we're the infrastructure layer making it possible.</p> <p>We call ourselves BANDmates because building something this big takes collaboration, different perspectives, and the freedom to bring your authentic self to the table.</p> <p>We're experts who speak like humans. Ready to build what's next? <strong>#JoinTheBand</strong></p> </div> <p><strong>What We Are Looking For:</strong></p> <p>As an <strong>Applied AI Engineer </strong>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.</p> <p><strong>What You'll Do:</strong></p> <ul> <li>Own and extend existing AI platforms and tooling, improving reliability, expanding capabilities, and integrating them more deeply with internal systems.</li> <li>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.</li> <li>Identify and build AI-powered tooling that creates leverage across the Corporate IT Engineering stack, including infrastructure, identity, monitoring, and automation platforms.</li> <li>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.</li> <li>Containerize and orchestrate AI workloads using Docker and Kubernetes, ensuring reliable and reproducible deployments across environments.</li> <li>Automate infrastructure provisioning and configuration using Terraform and Ansible, following infrastructure-as-code best practices.</li> <li>Establish AI development patterns and best practices across the Corporate IT Engineering organization, helping teams adopt AI capabilities effectively and responsibly.</li> <li>Stay current with the evolving AI and MLOps landscape and bring relevant advancements back to the team.</li> </ul> <p><strong>What You Need:</strong></p> <h3><span style="font-size: 10pt;"><strong>AI & Application Development</strong></span></h3> <ul> <li>Hands-on experience owning or extending LLM-powered platforms, including RAG pipeline development, prompt engineering, and integrating LLM APIs into production internal systems.</li> <li>Expert-level knowledge of AI infrastructure, including model serving, inference optimization, GPU/CPU resource management, and MLOps pipelines.</li> <li>Experience designing and building internal API layers or shared platform services that multiple teams and systems publish to and consume from.</li> <li>Proficiency in Python and/or TypeScript for building integrations, scripts, and lightweight internal services.</li> <li>Experience working with REST APIs and building integrations across a diverse internal tooling ecosystem.</li> </ul> <h3><span style="font-size: 10pt;"><strong>Cloud & Infrastructure </strong></span></h3> <ul> <li>Strong AWS experience: required proficiency in core services (EC2, ECS/EKS, S3, RDS, Lambda, IAM, VPC) and experience architecting and operating production workloads on AWS.</li> <li>Deep Docker and Kubernetes expertise: required hands-on experience containerizing applications, writing Dockerfiles, managing multi-container deployments, and orchestrating workloads with Kubernetes (EKS or self-managed).</li> <li>Deep Terraform and Ansible expertise: required experience writing and maintaining Terraform modules for cloud infrastructure, and using Ansible for configuration management and automation.</li> <li>Experience with GitHub for version control, pull request workflows, branching strategies, and CI/CD integration.</li> <li>Experience with Artifactory for artifact management, including publishing and consuming build artifacts, Docker images, and package registries.</li> </ul> <h3><span style="font-size: 10pt;"><strong>Mindset & Collaboration</strong></span></h3> <ul> <li>An experimental mindset: comfortable inheriting imperfect systems, iterating quickly, and improving as you go.</li> <li>Ability to evaluate AI capabilities through a business lens, understanding not just what’s possible but what creates real value for internal teams and the organization.</li> <li>Strong communication skills and the ability to explain AI concepts and tradeoffs to non-technical stakeholders.</li> <li>A collaborative, team-first approach with a genuine curiosity about where AI is headed.</li> <li>A Bachelor’s degree in Computer Science, Engineering, or equivalent hands-on experience.</li> </ul> <p><strong>Bonus Points:</strong></p> <ul> <li>E