Senior Staff AI-Native DevOps Engineer

Description

The Conversational AI team builds the core platform behind the company's conversational AI product. Applied scientists and software engineers work together here, bridging research and production.

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

This is a new DevOps practice, and you would lead it. The role blends deep cloud infrastructure, containerization, orchestration, and infrastructure-as-code skills with practical knowledge of how ML and LLM services run in production. You favour pragmatic engineering that measurably improves deployment speed and service reliability.

Responsibilities

• Own the building, deploying, and reliability of the systems behind the product, from commit to production across SaaS environments.
• Build 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, secure cloud-native applications and infrastructure.
• Solid software engineering skills, including building infrastructure tools and automation in Go, Python, or a similar language.
• A track record of leading complex infrastructure work, setting engineering standards, and getting several teams to adopt them.
• Strong skill diagnosing complex distributed systems problems, and a drive to improve performance, reliability, and operational efficiency.
• Good grasp of modern AI, including LLMs, generative AI, and agentic architectures, and how to apply them to engineering and operations.
• An AI-native mindset: using AI tools and automation to speed up development, raise operational efficiency, and build smarter, more resilient systems.
• AI strategy is a critical skill at this level, covering both how you build and what you build.
• Confident with AI coding assistants such as GitHub Copilot or Cursor, and review generated code as rigorously as any other, catching logic errors, security flaws, and missed edge cases.
• Skilled at using AI for debugging and trace analysis, with a clear understanding of the data privacy rules for what goes into a prompt.
• Understanding of LLM application architecture (model inference, RAG, tool calling, agentic workflows) and its infrastructure, scaling, and reliability implications.
• Ability to deploy, run, and troubleshoot AI services in production, including model API integrations, observability, latency, resource use, and operating costs.
• Deploy, scale, and run distributed microservices on production Kubernetes, covering application lifecycle, networking, security, and reliability.
• Automate infrastructure with IaC and Kubernetes-native tools such as Terraform, Ansible, Helm, and Kubernetes Operators.
• Build and maintain CI/CD pipelines and GitOps workflows with Jenkins, GitHub Actions, Tekton, and ArgoCD for testing, deployment, and release management.
• Set up observability, monitoring, and incident response, running root cause analysis and fixing issues to improve availability, fault tolerance, and resilience.
• Design and tune cloud infrastructure across public and private clouds, prioritising security, scale, cost, and simplicity.
• Minimum: at least 8 years of professional experience in software engineering, infrastructure engineering, DevOps, or a related field.
• Minimum: at least 6 years deploying and running containerized applications and distributed services in production.
• Minimum: at least 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.
• Minimum: hands-on use of AI-assisted development tools, with a proven ability to validate, debug, and safely integrate 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 operating Kubernetes-based SaaS platforms at scale, including multi-service deployments and complex production environments.
• Preferred: experience developing 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 establishing scalable platform engineering practices.
• Preferred: hands-on experience deploying, integrating, or operating LLM-powered applications or agentic systems in production, or building AI-enabled automation for observability, incident diagnosis, or remediation.

Conditions

Base pay for positions in this location is $181,200 - $317,100, plus equity when applicable, variable or incentive compensation, and benefits.

Base pay is a guideline. Individual total compensation varies with qualifications, skill level, competencies, and work location.

Compensation depends on the geographic location where the role sits and may change with that location.

Sales roles generally come with a competitive On Target Earnings (OTE) incentive structure.

Your work persona (flexible, remote, or required in office) is set by the nature of your work and your assigned location. The company may check the distance between your primary residence and the nearest office using a third-party service.

Benefits

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