AI AgentOps Engineer: Reliability & FinOps Lead (Singapore)

Accenture; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Accenture is seeking an AgentOps Engineer to keep AI-powered services running at scale. You will manage reliability, performance, and operational health of enterprise AI agents in production, monitor behavior, optimize costs, and oversee releases post go-live.

Responsibilities include observability design, incident response, and partnering with engineers and AMS teams to enhance reliability, security, and efficiency across enterprise platforms.

Qualifications

• Proven experience in DevOps, SRE, or production operations.
• Experience with monitoring, observability, and operational support tools.
• Experience managing incidents, releases, production support, and service operations.
• Strong understanding of reliability, performance, automation, and operational excellence.
• Excellent problem-solving and stakeholder management skills.

Responsibilities

• Operate, monitor, and support AI agents and platforms in production.
• Design and implement observability, monitoring, incident response, release management, and operational frameworks for AI solutions.
• Monitor agent accuracy, latency, availability, exception rates, user feedback, and token consumption.
• Manage prompt, workflow, and agent releases, including rollback strategies and readiness checks.
• Support AI FinOps activities through usage tracking, cost visibility, and reporting.
• Develop runbooks, reliability standards, and lifecycle controls for scalable AI services.
• Collaborate with engineers and AMS teams to improve reliability, security, and cost efficiency.

Skills

DevOps

SRE

Production ops

Monitoring tools

Incident management

Release management

Stakeholder management

Tools

CI/CD pipelines

Terraform

Bicep

AWS

Azure

GCP

Observability tools

Job description

Accenture is seeking an AgentOps Engineer to keep AI-powered services running at scale. You will manage reliability, performance, and operational health of enterprise AI agents in production, monitor behavior, optimize costs, and oversee releases post go-live.

Responsibilities include observability design, incident response, and partnering with engineers and AMS teams to enhance reliability, security, and efficiency across enterprise platforms.