Applied AI Engineer

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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.