AI MLOps Engineer - Generative AI Governance

About the role

Vinova, a development company founded in 2010, is recruiting an AI MLOps Engineer for a Singapore government agency. The agency runs a Case Management Platform built on OutSystems and is integrating generative AI and agentic AI into its applications. You will work with the agency's personnel, application teams and stakeholders, ensuring AI use on the AI Foundation Platform is trusted, controlled and compliant.

Key responsibilities

• AI governance and compliance: implement governance controls, ensure compliance with data security, PDPA and Responsible AI, and maintain evidence for ICT&SS/IM8, the System Security Plan and security reviews
• Monitoring and observability: model monitoring for accuracy, drift, latency and cost, audit logging, agent observability dashboards and alerting, and incident response procedures
• Evaluation and testing: accuracy validation, benchmarking, regression testing and human-in-the-loop review, plus agent evaluation and red-teaming
• Guardrails and security: configure content filtering, PII detection, prompt injection protection and Amazon Bedrock Guardrails, define agent-level controls, and run security tests
• MLOps: build and maintain pipelines for model and prompt versioning, deployment, rollback and lifecycle management
• Collaborate with AI Platform, AI/ML and DevSecOps engineers, and document governance standards, model cards and operating procedures

Required profile

• Degree or diploma in Computer Science, Data Science, Information Technology or a related discipline
• At least 2 years of experience in software engineering, MLOps, DevOps, data engineering or AI operations
• Good understanding of data security, privacy and Responsible AI, and of government ICT&SS policies (IM8, SSP)
• Nice to have: familiarity with Singapore's Model AI Governance Framework, AI Verify, IM8 and PDPA, GCC or security assessment experience, and AWS certifications

Required skills

• Python, with working knowledge of JavaScript/TypeScript for dashboards
• Model monitoring and evaluation tools such as Amazon CloudWatch, SageMaker Model Monitor, MLflow, Langfuse
• CI/CD pipelines, Git, Docker and Kubernetes
• Understanding of LLM evaluation metrics, red-teaming and agent tracing tools

What we offer

• 14 days of annual leave, paid sick leave and public holidays
• Group medical insurance and marriage, newborn and bereavement allowances
• Work equipment, mentorship and skill development through client projects
• Clear path to leadership roles