Staff Software Engineer - Infrastructure (AI Native)

Jobgether; United States; Remote; USD 180,000 - 280,000; Full time

Benefits offered by this job

Health coverage

Remote-first

Flexible time off

Global team

Technical growth

Job summary

Jobgether is seeking a staff-level infrastructure engineer to design and scale AI-powered agent systems and production-grade platforms. You will drive architecture decisions, mentor engineers, and lead resilient deployment practices across a remote-first, globally distributed team.

Responsibilities include building scalable cloud infrastructure, improving CI/CD, enhancing observability, and ensuring robust safeguards for AI agents in production.

Qualifications

• Demonstrated experience designing, building, and shipping AI agents or agentic systems that perform meaningful, real-world tasks.
• Strong understanding of infrastructure fundamentals, including distributed systems, cloud computing, Kubernetes, networking, and production operations.
• Experience taking infrastructure from early development stages to meaningful production scale, with the ability to explain architectural choices, trade-offs, and lessons learned.
• A reliability-focused engineering mindset, with a strong understanding of failure modes, blast radius, operational complexity, resilience, and recovery strategies.
• Ability to troubleshoot complex issues across multiple layers, from application behavior and distributed services to Kubernetes, networking, and cloud infrastructure.

Responsibilities

• Design, build, and improve scalable cloud infrastructure spanning Kubernetes, networking, compute, storage, and distributed systems.
• Identify performance bottlenecks, potential failure modes, operational risks, and infrastructure weaknesses before they affect production environments.
• Strengthen CI/CD pipelines, automated testing, deployment controls, and rollout strategies to support safer, more reliable releases.
• Build and enhance observability capabilities, including monitoring, alerting, distributed tracing, and operational tooling that improves system visibility and troubleshooting.
• Optimize infrastructure performance, reliability, resource utilization, and cloud expenditure.
• Design, develop, and deploy AI agents for infrastructure management and engineering workflows, enabling them to use tools and APIs, execute multi-step tasks, troubleshoot issues, and automate operational activities.
• Establish safeguards for AI agents operating in production, including evaluation frameworks, permission controls, guardrails, observability, state management, and failure recovery mechanisms.
• Build and maintain internal platforms and developer tools that improve engineering productivity, such as package registries, artifact stores, CI systems, and infrastructure automation solutions.
• Lead technical design discussions and contribute to architectural decisions, design documentation, code reviews, and engineering standards.
• Mentor engineers, share technical knowledge, and help raise the quality of implementation and operational practices across the team.
• Participate in the on-call rotation, respond to production incidents, and translate incident learnings into permanent fixes, runbooks, and automation that reduce recurring issues.
• Collaborate across engineering teams to build infrastructure that supports evolving AI workloads and enterprise-scale production requirements.

Skills

Cloud architecture

Kubernetes

Distributed systems

AI agent design

CI/CD pipelines

Technical leadership

Tools

AWS/EKS

FluxCD

Temporal

Grafana

ClickHouse

Go

Rust

Job description

Accountabilities

• Design, build, and improve scalable cloud infrastructure spanning Kubernetes, networking, compute, storage, and distributed systems.
• Identify performance bottlenecks, potential failure modes, operational risks, and infrastructure weaknesses before they affect production environments.
• Strengthen CI/CD pipelines, automated testing, deployment controls, and rollout strategies to support safer, more reliable releases.
• Build and enhance observability capabilities, including monitoring, alerting, distributed tracing, and operational tooling that improves system visibility and troubleshooting.
• Optimize infrastructure performance, reliability, resource utilization, and cloud expenditure.
• Design, develop, and deploy AI agents for infrastructure management and engineering workflows, enabling them to use tools and APIs, execute multi-step tasks, troubleshoot issues, and automate operational activities.
• Establish safeguards for AI agents operating in production, including evaluation frameworks, permission controls, guardrails, observability, state management, and failure recovery mechanisms.
• Build and maintain internal platforms and developer tools that improve engineering productivity, such as package registries, artifact stores, CI systems, and infrastructure automation solutions.
• Lead technical design discussions and contribute to architectural decisions, design documentation, code reviews, and engineering standards.
• Mentor engineers, share technical knowledge, and help raise the quality of implementation and operational practices across the team.
• Participate in the on-call rotation, respond to production incidents, and translate incident learnings into permanent fixes, runbooks, and automation that reduce recurring issues.
• Collaborate across engineering teams to build infrastructure that supports evolving AI workloads and enterprise-scale production requirements.

Requirements

• Demonstrated experience designing, building, and shipping AI agents or agentic systems that perform meaningful, real-world tasks. Experience limited to AI coding assistants or prompt experimentation is not sufficient.
• Strong understanding of infrastructure fundamentals, including distributed systems, cloud computing, Kubernetes, networking, and production operations.
• Experience taking infrastructure from early development stages to meaningful production scale, with the ability to explain architectural choices, trade-offs, and lessons learned.
• A reliability-focused engineering mindset, with a strong understanding of failure modes, blast radius, operational complexity, resilience, and recovery strategies.
• Ability to troubleshoot complex issues across multiple layers, from application behavior and distributed services to Kubernetes, networking, and cloud infrastructure.
• Experience automating repetitive operational tasks and developing tools or platforms that improve the productivity of other engineers.
• Experience building internal developer platforms or infrastructure systems, such as CI pipelines, artifact storage, package registries, or related tooling.
• Strong technical communication skills, including the ability to produce clear design documents, explain architectural decisions, lead technical reviews, and document operational learnings.
• Staff-level technical leadership skills, including mentoring engineers, influencing engineering standards, and guiding complex projects without relying solely on formal authority.
• Practical understanding of AI agent architecture, including tool usage, workflow orchestration, state or memory management, evaluation, guardrails, and agent reliability.
• Experience with multi-tenant SaaS platforms, infrastructure automation, distributed systems, or production-grade AI agents is particularly valuable.
• Familiarity with technologies such as AWS/EKS, Kubernetes, OpenTofu, FluxCD, Temporal, VictoriaMetrics, ClickHouse, Grafana, Go, or Rust is beneficial. Expertise across the entire stack is not required, provided you have strong fundamentals and can learn new technologies quickly.
• Ability to work effectively in a remote-first, asynchronous environment, taking ownership of technical decisions and collaborating with colleagues across locations.

Benefits

• Competitive compensation: Market-benchmarked base salary, performance-based variable pay, and impact-driven equity for most roles.
• Comprehensive health coverage: Health, dental, vision, and mental health benefits from day one, alongside flexible health stipends tailored to the relevant country.
• Flexible time off: Flexible vacation arrangements and modern leave policies designed to support personal well-being and work-life balance.
• Remote-first flexibility: Work remotely with a globally distributed team spanning more than 15 countries, within a high-trust, asynchronous working environment.
• AI-native culture: Work in an environment where AI is integrated into everyday engineering practices, tools, and decision-making.
• Accelerated professional growth: Develop expertise through complex technical challenges, cutting-edge technologies, and large-scale implementations.
• Technical ownership: Opportunities to influence infrastructure architecture, shape engineering practices, and build systems that support the next generation of AI applications.
• Collaborative global team: Learn from experienced professionals, share knowledge, and contribute to a culture that values technical excellence, inclusion, and continuous improvement.