Job description

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. 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. Mindset & Collaboration An experimental mindset: comfortable inheriting imperfect systems, iterating quickly, and improving as you go. 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. Strong communication skills and the ability to explain AI concepts and tradeoffs to non-technical stakeholders. A collaborative, team-first approach with a genuine curiosity about where AI is headed. A Bachelor’s degree in Computer Science, Engineering, or equivalent hands-on experience. Bonus Points: Experience with agentic frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar. Familiarity with corporate IT or infrastructure engineering environments, understanding how enterprise platforms around identity, monitoring, and automation operate. Background building MCP (Model Context Protocol) servers or tools that extend AI agent capabilities. Experience with vector databases (e.g., Pinecone, Weaviate, pgvector) and semantic search. Experience building or maintaining internal developer platforms, artifact registries, or shared API services. The Whole Person Promise: At Bandwidth, we’re pretty proud of our corporate culture, which is rooted in our “Whole Person Promise.” We promise all employees that they can have meaningful work AND a full life, and we provide a work environment geared toward enriching your body, mind, and spirit. How do we do that? Well… 100% company-paid Medical, Vision, & Dental coverage for you and your family with low deductibles and low out-of-pocket expenses. All new hires receive four weeks of PTO. PTO Embargo. When you take time off (of any kind!) you’re embargoed from working. Bandmates and managers are not allowed to interrupt your PTO – not even with email. Additional PTO can be earned throughout the year through volunteer hours and Bandwidth challenges. “Mahalo moments” program grants additional time off for life’s most important moments like graduations, buying a first home, getting married, wedding anniversaries (every five years), and the birth of a grandchild. 90-Minute Workout Lunches and unlimited meetings with our very own nutritionist. Are you excited about the position and its responsibilities, but not sure if you’re 100% qualified? Do you feel you can work to help us crush the mission? If you answered ‘yes’ to both of these questions, we encourage you to apply! You won’t want to miss the opportunity to be a part of the BAND. Applicant Privacy Notice

Requirements

Must-haves

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.
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 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.

Nice-to-haves

Experience with agentic frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
Familiarity with corporate IT or infrastructure engineering environments, understanding how enterprise platforms around identity, monitoring, and automation operate.
Background building MCP (Model Context Protocol) servers or tools that extend AI agent capabilities.
Experience with vector databases (e.g., Pinecone, Weaviate, pgvector) and semantic search.
Experience building or maintaining internal developer platforms, artifact registries, or shared API services.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapidockerkubernetesllmpythontypescriptrestawsci/cdgo

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Pto