Senior Staff AI-Native DevOps Engineer

Description

The Conversational AI team builds the core platform behind the company Otto, the company's conversational AI product. The team is a mix of applied scientists and software engineers who work across research and production.

The team's scope covers agent orchestration, including harness engineering, prompt tuning, agent skills, policy design, and multilingual quality evaluation; the A2A and MCP protocols that let agents work together and reach tools; background channels and conversational triggers that start agent actions; AI-native licensing and pricing; and the central caching layer linking the company platform to the services behind Otto.

The team is starting a new DevOps practice and is hiring a Staff AI-Native DevOps Engineer to lead it. The role is a senior, hands-on leadership position with a bias toward practical engineering that measurably improves deployment speed and service reliability.

Responsibilities

• Own the build, deployment, and reliability of the systems behind Otto, from commit to production across the SaaS environments.
• Build and run CI/CD pipelines, release automation, Kubernetes infrastructure, observability, and operational tooling so that agent orchestration, MCP and A2A services, and the caching layer can ship safely and often.
• Work with applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure.

Requirements

• Deep experience designing, building, and running scalable, reliable, and secure cloud-native applications and infrastructure.
• Strong software engineering skills, including building infrastructure tooling and automation in Go, Python, or similar languages.
• Experience leading complex infrastructure work, setting engineering standards, and getting multiple teams to adopt them.
• Strong problem-solving skills for diagnosing distributed systems failures and improving performance, reliability, and operational efficiency.
• Solid understanding of LLMs, generative AI, and agentic architectures, and how to apply them to engineering and operations problems.
• An AI-native mindset: you use AI tools and automation on your own initiative to speed up development, run operations more efficiently, and build smarter, more resilient systems.
• DevOps: you deploy, scale, and operate distributed microservices on production Kubernetes, covering application lifecycle, networking, security, and reliability.
• DevOps: you design and implement infrastructure automation with Infrastructure as Code and Kubernetes-native tools such as Terraform, Ansible, Helm, and Kubernetes Operators.
• DevOps: you build and maintain CI/CD pipelines and GitOps workflows with tools such as Jenkins, GitHub Actions, Tekton, and ArgoCD to automate testing, deployment, and releases.
• DevOps: you set up observability, monitoring, and incident response, perform root cause analysis, and drive fixes that improve availability, fault tolerance, and resilience.
• DevOps: you architect and tune cloud infrastructure across public and private clouds, prioritizing security, scalability, cost, and simplicity.
• AI skills: AI strategy is a core skill at this level, covering both how you build and what you build.
• AI skills: you use AI coding assistants such as GitHub Copilot or Cursor and review their output as carefully as any other code, catching logic errors, security problems, and missed edge cases.
• AI skills: you use AI to help debug and analyze traces, and you understand the data privacy rules that govern what can go into a prompt.
• AI skills: you understand LLM-powered application design, including model inference, retrieval-augmented generation (RAG), tool calling, and agentic workflows, and their effects on infrastructure, scale, and reliability.
• AI skills: you can deploy, operate, and troubleshoot AI-powered services in production, including model API integrations, observability, latency, resource use, and operating cost.
• Minimum: 8+ years of professional experience in software engineering, infrastructure engineering, DevOps, or a related field.
• Minimum: 6+ years deploying and operating containerized applications and distributed services in production.
• Minimum: 4+ years of hands-on work with Kubernetes, cloud infrastructure, and infrastructure automation.
• Minimum: proficiency in at least one programming or scripting language such as Go, Python, or Bash, with experience building production-grade automation and tooling.
• Minimum: practical experience with AI-assisted development tools, and a proven ability to validate, debug, and safely integrate AI-generated code.
• Minimum: working knowledge of LLMs, generative AI, and agentic AI concepts, including integration patterns and operational considerations.
• Preferred: 12+ years of experience in software engineering, infrastructure engineering, DevOps, or platform engineering.
• Preferred: extensive experience architecting and running Kubernetes-based SaaS platforms at scale, including multi-service deployments and complex production environments.
• Preferred: experience building Kubernetes Operators, custom controllers, or other Kubernetes-native automation for application lifecycle and infrastructure tasks.
• Preferred: a track record of leading cross-team infrastructure initiatives, improving production reliability, and building scalable platform engineering practices.
• Preferred: hands-on experience deploying, integrating, or running LLM-powered applications or agentic systems in production. Experience building AI-enabled infrastructure automation, observability, incident diagnosis, or remediation workflows to improve reliability and engineering productivity.

Conditions

Base pay for this location is $201,300 to $352,300, plus equity where applicable, variable or incentive pay, and benefits. Pay is set by location, and the base shown is a guideline; actual total compensation depends on qualifications, skill level, competencies, and work location.

Work persona (flexible, remote, or required in office) is assigned based on the nature of the work and the employee's work location. Eligibility may be checked by measuring the distance between the employee's home and the nearest company office using a third-party service.

The company is an equal opportunity employer and considers applicants without regard to protected characteristics.

Benefits

• Health plans, including flexible spending accounts.
• 401(k) plan with company match.
• Employee stock purchase plan (ESPP).
• Matching donations.
• Flexible time away and family leave programs.