Applied AI Engineer

Bandwidth • Raleigh, NC

2d

About The Position

As an Applied AI Engineer on Bandwidth’s Corporate IT Engineering team, you will identify opportunities to apply AI across internal systems and operations and build solutions that create practical value. This is an infrastructure and systems role with a deep AI focus, embedding AI into the platforms and workflows that support Bandwidth’s operations.

You will own and extend AI platforms and tooling, build internal API layers and shared services, develop AI-powered tools and proof-of-concepts, and deploy AI workloads reliably across environments. You will also help establish AI development practices across the organization, evaluate evolving AI and MLOps advancements, and work collaboratively to help teams adopt AI capabilities effectively and responsibly.

Requirements

• Hands-on experience owning or extending LLM-powered platforms, including RAG pipeline development, prompt engineering, and integration of 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 used by multiple teams and systems.
• Proficiency in Python and/or TypeScript for integrations, scripts, and lightweight internal services.
• Experience working with REST APIs and building integrations across diverse internal tooling.
• Strong AWS experience, including proficiency with EC2, ECS/EKS, S3, RDS, Lambda, IAM, and VPC, and experience architecting and operating production workloads on AWS.
• Hands-on Docker and Kubernetes experience, including containerizing applications, writing Dockerfiles, managing multi-container deployments, and orchestrating workloads with Kubernetes (EKS or self-managed).
• 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.
• An experimental mindset, comfort inheriting imperfect systems, and the ability to iterate quickly and improve systems over time.
• Ability to evaluate AI capabilities from a business perspective and understand what creates 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 and genuine curiosity about the direction of AI.
• A Bachelor’s degree in Computer Science or Engineering, or equivalent hands-on experience.

Nice To Haves

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

Responsibilities

• Own and extend existing AI platforms and tooling, improving reliability, expanding capabilities, and integrating them with internal systems.
• Architect and build internal API layers and shared services that enable AI workflows and internal applications to publish, version, and retrieve outputs across the engineering ecosystem.
• Identify and build AI-powered tooling across the Corporate IT Engineering stack, including infrastructure, identity, monitoring, and automation platforms.
• Develop and iterate on proof-of-concepts demonstrating how AI can augment or automate internal workflows, such as infrastructure-log anomaly detection, AI-assisted documentation, and IT troubleshooting.
• Containerize and orchestrate AI workloads using Docker and Kubernetes to support reliable, 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 Corporate IT Engineering, helping teams adopt AI capabilities effectively and responsibly.
• Stay current with developments in AI and MLOps and bring relevant advancements to the team.

Benefits

• 100% company-paid medical, vision, and dental coverage for employees and their families, with low deductibles and low out-of-pocket expenses.
• Four weeks of PTO for all new hires.
• PTO embargo policy: employees are not to be interrupted or contacted for work while taking PTO.
• Additional PTO may be earned through volunteer hours and Bandwidth challenges.
• Additional time off through the “Mahalo moments” program for significant life events.
• 90-minute workout lunches.
• Unlimited meetings with an in-house nutritionist.

What This Job Offers

• Job Type: Full-time