Software Engineer - AI Tooling Infrastructure

Jobgether; España; On-site; EUR 70,000 - 105,000; Full time

Job summary

Jobgether is seeking an experienced Software Engineer to build, operate, and scale backend infrastructure powering AI-driven developer tools. You will own production services, including MCP integrations, with a focus on reliability, security, and observability.

You will collaborate with platform, security, and infrastructure teams in a globally distributed environment, taking significant ownership over systems in production and contributing to open-source tooling.

Qualifications

• 5+ years building, deploying, and operating production backend services.
• Strong programming in TypeScript or Go, with the other language knowledge.
• Experience designing and maintaining APIs with versioning, error handling, and rate limiting.
• Understanding OAuth, OpenID Connect (OIDC), JWT, and API authorization in production.
• Good knowledge of PostgreSQL and relational databases.
• Experience with CI/CD pipelines, Docker, containerized deployments, and Infrastructure as Code (IaC).
• Ability to troubleshoot distributed systems using metrics, logs, and traces.
• Strong written and verbal communication across globally distributed teams.
• Adaptability, ownership, and ability to navigate ambiguity in fast-paced environments.

Responsibilities

• Build, deploy, operate, and scale production backend services supporting AI-powered developer tools.
• Own service architecture and infrastructure decisions, improving reliability, maintainability, scalability, and deployment processes.
• Define and monitor service reliability objectives (SLOs), track error budgets, and prioritize improvements with SRE teams.
• Participate in the team's on-call rotation, investigate incidents, coordinate resolutions, and develop runbooks.
• Manage operational support requests and identify recurring issues for long-term improvements.
• Design and maintain authentication systems for AI agents, including OAuth applications and tokens.
• Establish and maintain observability through metrics, tracing, logging, dashboards, and alerts.
• Manage AI agent traffic with rate limits, capacity controls, and safeguards against spikes.
• Collaborate with Platform, SRE, Authentication, Billing, and Edge Functions teams to improve infra and releases.
• Contribute to AI tooling infrastructure ensuring reliability, security, and performance.

Skills

TypeScript

Go

APIs design

OAuth/OIDC/JWT

Distributed systems

CI/CD

PostgreSQL

Observability

On-call

Communication

Tools

Docker

Kubernetes

OpenTelemetry

Grafana

Logflare

CI/CD tools

Job description

We are looking for an experienced Software Engineer to build, operate, and scale the backend infrastructure powering innovative AI-driven developer tools. In this role, you will take ownership of production services, including infrastructure supporting AI agents, developer assistants, and Model Context Protocol (MCP) integrations. You will focus on ensuring reliability, security, performance, and observability in an environment where AI-driven traffic introduces unique infrastructure challenges. Working closely with platform, security, and infrastructure teams, you will help shape scalable architectures and improve operational excellence. This is a hands‑on engineering position offering significant ownership over systems running in production. You will join a globally distributed, asynchronous engineering environment that values autonomy, open‑source development, and continuous improvement.

Responsibilities

• Build, deploy, operate, and scale production backend services supporting AI-powered developer tools.
• Own service architecture and infrastructure decisions, improving system reliability, maintainability, scalability, and deployment processes.
• Define and monitor service reliability objectives (SLOs), track error budgets, and prioritize improvements in collaboration with Site Reliability Engineering (SRE) teams.
• Participate in the team's on-call rotation, investigate production incidents, coordinate resolutions, and develop incident reports, runbooks, and preventive solutions.
• Manage operational support requests and identify recurring issues that can be addressed through long‑term engineering improvements.
• Design and maintain authentication and authorization systems for AI agents and machine clients, including OAuth applications, scoped tokens, permissions, and secure access management.
• Establish and maintain comprehensive observability through metrics, distributed tracing, logging, dashboards, and alerting systems.
• Manage AI agent traffic, implementing rate limits, capacity controls, cost safeguards, and mechanisms to handle dependency failures and unexpected traffic spikes.
• Collaborate with Platform, SRE, Authentication, Billing, and Edge Functions teams to improve shared infrastructure, release processes, security, and operational practices.
• Contribute to the development of AI tooling infrastructure, ensuring that reliability, security, and performance remain central to engineering decisions.

Mandatory Qualifications

• At least 5 years of experience building, deploying, and operating production backend services, including participation in on‑call rotations.
• Strong programming skills in TypeScript or Go, with practical working knowledge of the other language.
• Proven experience designing and maintaining APIs, including versioning, error handling, rate limiting, and backward compatibility.
• Strong understanding of OAuth, OpenID Connect (OIDC), JWT, and API authorization, with the ability to troubleshoot authentication and permission issues in production environments.
• Good knowledge of PostgreSQL and relational database systems.
• Experience with CI/CD pipelines, Docker, containerized deployments, and Infrastructure as Code (IaC).
• Ability to troubleshoot distributed systems using metrics, logs, and traces to identify performance bottlenecks and service failures.
• Strong written and verbal communication skills, with the ability to collaborate across teams and work effectively in globally distributed, asynchronous environments.
• Adaptability, ownership, and the ability to navigate ambiguity while delivering solutions in fast‑paced engineering environments.

Preferred Qualifications

• Experience with AI agent tooling, Model Context Protocol (MCP), or infrastructure serving AI agents and LLM clients.
• Experience extracting services from larger codebases while maintaining compatibility with existing integrations.
• Familiarity with observability platforms such as Grafana, OpenTelemetry, or Logflare, along with practical experience implementing SLOs.
• Contributions to open-source projects, particularly within the Node.js, Go, TypeScript, or API tooling ecosystems.