Sr. Software Developer; Infrastructure

About the Role

Sr. Software Developer (Infrastructure) – you will help build the platform and tools that enable Bandwidth engineers to ship better software, faster, and enable AI‑first work.

• Design and operate Bandwidth's AI infrastructure layer, including the LLM gateway, model routing, and cost controls.
• Build and maintain developer tooling and platform services that help engineers ship faster with less friction.
• Drive adoption of AI coding tools by building enablement systems, prompt libraries, and internal tooling.
• Integrate AI with existing systems such as Jira, Confluence, Slack, and Git Hub.
• Measure engineering metrics such as velocity, build times, reversion rates, and shipping outcomes.
• Advance agentic workflows that enable teams to orchestrate AI systems instead of writing every line of code.

• 5+ years of experience building and operating web services in production.
• Hands‑on experience with cloud infrastructure, especially AWS.
• Strong coding skills in Python, Go, Type Script, or similar.
• Experience with platform services: CI/CD, secrets management, developer portals, internal tooling.
• Familiarity with LLM APIs and tradeoffs involved in model routing, token cost management, and AI observability.
• Experience building tools or services that other engineers depend on day‑to‑day.
• Results‑driven mindset: you close the loop, ship, and measure.
• Bachelor’s degree or equivalent experience.

• Built or operated an LLM proxy, AI gateway, or model routing layer.
• Worked on a Developer Experience or Software Platform team before.
• Written infrastructure as code (Terraform, CDK, Cloud Formation).
• Integrated AI tools with internal systems like Jira, Slack, or Git Hub.
• Built services used by other developers (open‑source counts).
• Worked in a startup or high‑ambiguity environment requiring self‑definition of requirements.

• The AI landscape is moving fast; this team builds the infrastructure layer that supports every tool.
• Focus is on standardizing infrastructure, not tools, enabling an engineering organization where AI is woven into daily work and engineers deeply understand the systems they build.