- Singapore
Accenture Hiring! Full Time AgentOps Engineer – AI Managed Services in - Ricebowl
Working Location
Job Description
Responsibilities
Location: Singapore
About the Role
Keep AI-powered services running at scale. As an AgentOps Engineer, you will manage the reliability, performance, and operational health of enterprise AI agents in production. You will play a critical role in monitoring agent behavior, optimizing costs, managing releases, and ensuring AI solutions remain secure, reliable, and business-ready after go-live.
Key Responsibilities
- Operate, monitor, and support AI agents, copilots, and Agentic Managed Services platforms in production environments.
- Design and implement observability, monitoring, incident response, release management, and operational support frameworks for AI solutions.
- Monitor agent accuracy, latency, availability, exception rates, user feedback, token consumption, and operational performance.
- Manage prompt, workflow, and agent releases, including rollback strategies, production controls, and readiness checks.
- Support AI FinOps activities through usage tracking, cost visibility, performance optimization, and consumption reporting.
- Develop operational runbooks, reliability standards, and lifecycle controls to ensure scalable and supportable AI services.
- Partner with Agentic Engineers, AI Quality Engineers, Solution Architects, and AMS teams to continuously improve reliability, security, and cost efficiency.
Required Qualifications
- Proven experience in DevOps, Site Reliability Engineering (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 practices.
- Excellent problem-solving and stakeholder management skills.
Preferred Qualifications
- Experience supporting AI, Generative AI, copilots, agents, or intelligent automation platforms.
- Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
- Experience with CI/CD pipelines, Infrastructure as Code (Terraform, Bicep, or equivalent), and platform engineering practices.
- Familiarity with AI observability, AI FinOps, cost optimization, and performance monitoring.
- Experience working with enterprise platforms such as SAP, Salesforce, ServiceNow, or SuccessFactors.
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