Applied AI Engineer

Who We Are: Bandwidth , a prior “Best of EC” award winner, is a global software company that helps enterprises deliver exceptional experiences through voice, messaging, and emergency services. Reaching 65+ countries and over 90 percent of the global economy, we're the only provider offering an owned communications cloud that delivers advanced automation, AI integrations, global reach, and premium human support. Bandwidth is trusted for mission-critical communications by the Global 2000, hyperscalers, and SaaS builders! At Bandwidth, your music matters when you are part of the BAND. We celebrate differences and encourage BANDmates to be their authentic selves. #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. Proficiency in Python and/or TypeScript for building integrations, scripts, and lightweight internal services. Experience working with REST APIs and building integrations across a diverse internal tooling ecosystem. Cloud Infrastructure 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. 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). Deep Terraform and Ansible expertise: required experience writing and maintaining Terraform modules for cloud infrastructure, and using Ansible for configuration management and automation. Experience with GitHub for version control, pull request workflows, branching strategies, and CI/CD integration. Experience with Artifactory for artifact management, including publishing and consuming build artifacts, Docker images, and package registries. ... (truncated, view full listing at source)