Staff AI-Native DevOps Engineer

Description

The Conversational AI team at the company builds the core platform behind the company Otto, its conversational AI product. The group includes applied scientists and software engineers who work between research and production.

The team covers agent orchestration (harness engineering, prompt tuning, agent skills, policy design, and multi-language 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 a central caching layer linking the company platform to the services behind Otto.

This is a new DevOps practice inside the Conversational AI team, and the Staff AI-Native DevOps Engineer will own build, deployment, and reliability for Otto's systems, from commit to production in the SaaS environments. You will work alongside applied scientists and software engineers to make AI-native delivery repeatable, measurable, and secure. The role suits someone who knows cloud infrastructure, containers, orchestration, and infrastructure as code, understands how ML and LLM services behave in production, and prefers practical engineering that measurably improves deployment speed and reliability.

Responsibilities

• Own 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.
• Partner with applied scientists and software engineers to make delivery repeatable, measurable, and secure.

Requirements

• Show deep experience designing, building, and running scalable, reliable, secure cloud-native systems and infrastructure.
• Build infrastructure tooling and automation in Go, Python, or a similar language.
• Lead complex infrastructure work, set engineering standards, and get several teams to adopt them.
• Diagnose hard distributed-systems problems and improve performance, reliability, and operational efficiency.
• Understand modern AI, including LLMs, generative AI, and agentic architectures, and apply them to engineering and operations problems.
• Use AI tools and automation to speed up development, improve operations, and build more resilient systems.
• Treat AI strategy as a core skill at this level, in both how you build and what you build.
• 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.
• Use AI to help debug and analyse traces, and know the data privacy rules for what can go into a prompt.
• Understand LLM application architectures, including inference, retrieval-augmented generation (RAG), tool calling, and agentic workflows, and their effects on infrastructure, scale, and reliability.
• Deploy, run, and troubleshoot AI-powered services in production, including model API integrations, observability, latency, resource use, and running costs.
• Run distributed microservices on production Kubernetes, including application lifecycle, networking, security, and reliability.
• Automate infrastructure with Infrastructure as Code and Kubernetes-native tools such as Terraform, Ansible, Helm, and Kubernetes Operators.
• Build and maintain CI/CD and GitOps workflows with Jenkins, GitHub Actions, Tekton, and ArgoCD for 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 optimise cloud infrastructure across public and private clouds, balancing security, scale, cost, and simplicity.
• Minimum: 8+ years of professional experience in software engineering, infrastructure, DevOps, or a related field.
• Minimum: 6+ years running containerised 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 language or scripting tool such as Go, Python, or Bash, with experience building production-grade automation.
• Minimum: practical use of AI-assisted development tools, with a record of validating, debugging, and safely integrating 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, DevOps, or platform engineering.
• Preferred: extensive experience architecting and running Kubernetes-based SaaS platforms at scale, with multi-service deployments in complex production settings.
• Preferred: experience building Kubernetes Operators, custom controllers, or other Kubernetes-native automation for application lifecycle and infrastructure tasks.
• Preferred: a record of leading cross-team infrastructure work, improving production reliability, and building scalable platform engineering practices.
• Preferred: experience deploying, integrating, or running LLM-powered applications or agentic systems in production, and building AI-enabled automation for observability, incident diagnosis, or remediation to improve reliability and productivity.

Conditions

Base pay for this role in this location is $158,500 - $277,400, with equity where applicable, variable or incentive pay, and benefits. The base figure is a guide, and actual total pay depends on qualifications, skill level, competencies, and work location.

Pay depends on the geographic location where the role sits and may change with 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. The company may check the distance between your home and the nearest office with a third-party service.

Equal-opportunity statement omitted for brevity.

Benefits

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