Applied AI Engineer

Bandwidth • Raleigh, NC

4d

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 the resulting solutions. This is an infrastructure and systems role with a deep AI focus, embedding AI into the platforms and workflows that support Bandwidth.

You will own and extend AI platforms and tooling, develop shared API services, and build AI-powered tools across infrastructure, identity, monitoring, and automation. You will prototype AI applications for internal workflows, deploy AI workloads using containers and Kubernetes, and automate infrastructure with Terraform and Ansible. You will also establish AI development practices across the organization and stay current with relevant advances in AI and MLOps.

Requirements

• 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 used by multiple teams and systems.
• 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.
• 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 expertise, including containerizing applications, writing Dockerfiles, managing multi-container deployments, and orchestrating workloads with Kubernetes (EKS or self-managed).
• Terraform and Ansible expertise, including writing and maintaining Terraform modules for cloud infrastructure and using Ansible for configuration management and automation.
• Experience with GitHub 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 and comfort inheriting imperfect systems, iterating quickly, and improving them over time.
• Ability to evaluate AI capabilities through a business lens and understand what creates real value for internal teams and the organization.
• Strong communication skills, including the ability to explain AI concepts and tradeoffs to non-technical stakeholders.
• A collaborative, team-first approach and genuine curiosity about the future of AI.
• Bachelor’s degree in Computer Science, 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.
• Background 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 for publishing, versioning, and retrieving AI workflow 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 that demonstrate 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 for 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 the evolving AI and MLOps landscape and bring relevant advancements back 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: employees are not to be interrupted with work during time off.
• Additional PTO can be earned through volunteer hours and Bandwidth challenges.
• Mahalo moments provide additional time off for significant life events, including graduations, buying a first home, getting married, wedding anniversaries every five years, and the birth of a grandchild.
• 90-minute workout lunches.
• Access to an in-house nutritionist, with unlimited meetings.

What This Job Offers

• Job Type: Full-time