Staff AI-Native DevOps Engineer

Description

The Conversational AI team builds the core platform behind the company's conversational AI product, Otto. The team combines applied scientists and software engineers who work between research and production.

The platform covers agent orchestration (harness engineering, prompt tuning, agent skills, policy design, and quality evaluation across languages), the A2A and MCP protocols for agent collaboration and tool access, background channels and conversational triggers, AI-native licensing and pricing, and a central caching layer linking the company's platform to the services behind Otto.

The team is creating a new DevOps practice and is hiring a Staff AI-Native DevOps Engineer. You will own build, deployment, and reliability for Otto's systems, from commit to production across SaaS environments.

Your scope covers 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. You will work alongside scientists and engineers to make AI-native delivery repeatable, measurable, and secure.

The role needs strong cloud infrastructure, containerization, orchestration, and infrastructure-as-code skills, plus a working understanding of how ML and LLM services behave in production. You should favor practical engineering that measurably improves deployment speed and service reliability.

Responsibilities

• Deploy, scale, and operate distributed microservices in production Kubernetes environments, including application lifecycle, networking, security, and reliability.
• Build infrastructure automation with Infrastructure as Code and Kubernetes-native tools such as Terraform, Ansible, Helm, and Kubernetes Operators.
• 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.
• Set up observability, monitoring, and incident response, run root cause analysis, and drive fixes that improve availability, fault tolerance, and resilience.
• Design and tune cloud infrastructure across public and private clouds, prioritizing security, scalability, cost efficiency, and simplicity.

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 best practices, and getting adoption across several teams.
• Strong problem-solving skills for diagnosing complex distributed systems issues and improving performance, reliability, and operational efficiency.
• Solid understanding of modern AI, including LLMs, generative AI, and agentic architectures, and how to apply them to engineering and operations.
• An AI-native mindset: you use AI tools and automation to speed up development, improve operations, and build more intelligent, resilient systems.
• AI strategy is a core skill at this level, covering both how you build and what you build.
• Confident use of AI coding assistants such as GitHub Copilot or Cursor, with the discipline to review generated code as carefully as any other code, catching logic errors, security anti-patterns, and missed edge cases.
• Skill in AI-assisted debugging and trace analysis, and a clear grasp of the data privacy rules for what can go into a prompt.
• Understanding of LLM application architectures, including model inference, retrieval-augmented generation (RAG), tool calling, and agentic workflows, and their infrastructure, scalability, and reliability implications.
• Ability to deploy, operate, and troubleshoot AI-powered services in production, including model API integrations, observability, latency, resource use, and operating costs.
• 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 use of AI-assisted development tools, with proven ability to validate, debug, and safely integrate AI-generated code.
• Minimum: working knowledge of LLMs, generative AI, and agentic AI, including integration patterns and operational concerns.
• Preferred: 8+ years 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 setups.
• Preferred: experience building Kubernetes Operators, custom controllers, or other Kubernetes-native automation for application lifecycle and infrastructure operations.
• Preferred: proven technical leadership on cross-team infrastructure initiatives, improving production reliability, and building scalable platform engineering practices.
• Preferred: hands-on experience deploying, integrating, or operating LLM-powered applications or agentic systems in production. Experience building AI-enabled infrastructure automation, observability, incident diagnosis, or remediation workflows is a plus.

Conditions

Base pay for this location is $176,100 - $308,200, plus equity where applicable, variable or incentive compensation, and benefits. The base pay is a guideline; individual total compensation depends on qualifications, skill level, competencies, and work location. Pay is set by the geographic location of the role and may change with work location.

Sales positions generally include an On Target Earnings (OTE) incentive structure.

Work persona (flexible, remote, or required in office) is assigned based on the nature of the work and the employee's work location. The company may use a third-party service to check the distance between your primary residence and the nearest office.

The company is committed to equal opportunity in employment.

Benefits

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