- Toa Payoh Central Region (Singapore) Singapore
VINOVA PTE. LTD. Hiring! Full Time Backend Engineer in Central Region (Singapore), Earn up to SGD 7,800 - Ricebowl
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Job Description
Responsibilities
AI governance & compliance
Implement and maintain governance controls for AI models and services on the AI Foundation Platform, so AI use is trusted, controlled and compliant.
Ensure AI services comply with data security, data classification, privacy (PDPA) and Responsible AI requirements.
Maintain compliance evidence for ICT&SS / IM8, the System Security Plan (SSP), PDPA and Responsible AI requirements for agentic AI, and support security reviews and ARB submissions.
Monitoring & observability
Implement model monitoring covering accuracy, drift, hallucination, latency, cost and usage across AI applications.
Implement audit logging and traceability of model inputs, outputs and usage to support accountability and audit requirements.
Implement end-to-end agent observability and audit trails (traces of prompts, reasoning steps, tool calls, outputs and approvals), with dashboards and alerts mapped to SLIs/SLOs and risk thresholds.
Develop full-stack monitoring dashboards and reporting tools that give stakeholders visibility of AI performance, risks and compliance status.
Set up alerts and incident response procedures for AI service anomalies and degradation.
Evaluation & testing
Develop and run accuracy validation and evaluation processes, including test datasets, benchmarking, regression testing and human-in-the-loop review for model and prompt changes.
Evaluate agent behaviour, including task success, tool-use correctness, trajectory review, groundedness and regression testing before changes go live.
Guardrails & security
Configure and maintain AI security and safety guardrails, such as content filtering, PII detection and redaction, prompt injection protection and Amazon Bedrock Guardrails, in line with the agency's AI security governance.
Define and enforce agent-level guardrails and controls: tool allow-lists, permission and action limits, human approval for high-impact actions, limits on loops, steps and spend, and an emergency stop, aligned with the agency's AI security governance.
Run red-teaming and security testing for agentic threats, such as prompt injection through tool outputs, excessive agency, data leakage and misuse of tools, and track remediation.
Perform security checks and support risk assessments for AI components.
MLOps
Build and maintain MLOps pipelines for model and prompt versioning, deployment, rollback and lifecycle management.
Collaboration & documentation
Work with the AI Platform Engineer, AI/ML Engineers and DevSecOps Engineers to embed governance controls into the AI development lifecycle.
Work under the guidance of the Backend Senior Engineer and Solution Architect on guardrail design, risk decisions and the AI governance standards.
Maintain documentation of AI governance standards, model cards, operating procedures and compliance records.
What we are looking for
Education & experience
Degree or Diploma in Computer Science, Data Science, Information Technology or related disciplines.
At least 2 years of experience relevant to software engineering, MLOps, DevOps, data engineering or AI operations.
Programming
Good programming skills in Python, with working knowledge of JavaScript/TypeScript for building dashboards or web tools.
AI monitoring & evaluation
Experience with model monitoring, evaluation or observability tools such as Amazon CloudWatch, SageMaker Model Monitor, MLflow, Langfuse or equivalent.
Understanding of LLM evaluation techniques, such as groundedness and faithfulness metrics, LLM-as-judge and red-teaming.
Familiarity with Amazon Bedrock features such as Guardrails, Model Evaluation and model invocation logging.
Agentic AI
Understanding of agentic AI risks and controls, such as excessive agency, prompt injection, tool misuse and human-in-the-loop design, and of agent evaluation and tracing tools (e.g. Langfuse, OpenTelemetry).
DevOps
Experience with CI/CD pipelines and version control systems such as Git.
Knowledge of container technologies such as Docker and Kubernetes.
Security, privacy & government policy
Good understanding of data security, privacy and Responsible AI principles.
Understanding of Government ICT&SS management policies (IM8), SSP requirements and observability practices, and has applied them to AI guardrails, audit logging and monitoring.
Nice to have
Familiarity with Singapore's Model AI Governance Framework, AI Verify, IM8 and PDPA.
Experience with Government Commercial Cloud (GCC) or security assessments for government projects.
AWS certifications such as Machine Learning Engineer Associate, AI Practitioner or Cloud Practitioner.
Personal attributes
Strong attention to detail and a risk-aware, quality-driven mindset.
Comfortable with Agile methodologies and working closely with product development teams.
Strong on collaboration and communication, including documentation.
Established in 2010, Vinova is a passion-driven and award-winning development company for mobile, web, and enterprise applications. Our clients span the globe and cover a wide range of projects including IoT, blockchain, banking, fintech, networking, and ecommerce.
Vinova earns respect from customers by being committed to quality, flexibility, and speed. We strive to build great products by working with a passion, working closely with our customers, and through open but disciplined collaboration.
You will be working in an encouraging and challenging environment, taking advantage of the latest technology to develop excellent mobile apps for clients. You will be engaging clients in both on-site and off-site support.
Interested to join our all-star team? Submit your application to us now! Tell us why you want to work with us and why you think you’d be a good candidate. We are looking forward to hearing from you!
Implement and maintain governance controls for AI models and services on the AI Foundation Platform, so AI use is trusted, controlled and compliant.
Ensure AI services comply with data security, data classification, privacy (PDPA) and Responsible AI requirements.
Maintain compliance evidence for ICT&SS / IM8, the System Security Plan (SSP), PDPA and Responsible AI requirements for agentic AI, and support security reviews and ARB submissions.
Monitoring & observability
Implement model monitoring covering accuracy, drift, hallucination, latency, cost and usage across AI applications.
Implement audit logging and traceability of model inputs, outputs and usage to support accountability and audit requirements.
Implement end-to-end agent observability and audit trails (traces of prompts, reasoning steps, tool calls, outputs and approvals), with dashboards and alerts mapped to SLIs/SLOs and risk thresholds.
Develop full-stack monitoring dashboards and reporting tools that give stakeholders visibility of AI performance, risks and compliance status.
Set up alerts and incident response procedures for AI service anomalies and degradation.
Evaluation & testing
Develop and run accuracy validation and evaluation processes, including test datasets, benchmarking, regression testing and human-in-the-loop review for model and prompt changes.
Evaluate agent behaviour, including task success, tool-use correctness, trajectory review, groundedness and regression testing before changes go live.
Guardrails & security
Configure and maintain AI security and safety guardrails, such as content filtering, PII detection and redaction, prompt injection protection and Amazon Bedrock Guardrails, in line with the agency's AI security governance.
Define and enforce agent-level guardrails and controls: tool allow-lists, permission and action limits, human approval for high-impact actions, limits on loops, steps and spend, and an emergency stop, aligned with the agency's AI security governance.
Run red-teaming and security testing for agentic threats, such as prompt injection through tool outputs, excessive agency, data leakage and misuse of tools, and track remediation.
Perform security checks and support risk assessments for AI components.
MLOps
Build and maintain MLOps pipelines for model and prompt versioning, deployment, rollback and lifecycle management.
Collaboration & documentation
Work with the AI Platform Engineer, AI/ML Engineers and DevSecOps Engineers to embed governance controls into the AI development lifecycle.
Work under the guidance of the Backend Senior Engineer and Solution Architect on guardrail design, risk decisions and the AI governance standards.
Maintain documentation of AI governance standards, model cards, operating procedures and compliance records.
What we are looking for
Education & experience
Degree or Diploma in Computer Science, Data Science, Information Technology or related disciplines.
At least 2 years of experience relevant to software engineering, MLOps, DevOps, data engineering or AI operations.
Programming
Good programming skills in Python, with working knowledge of JavaScript/TypeScript for building dashboards or web tools.
AI monitoring & evaluation
Experience with model monitoring, evaluation or observability tools such as Amazon CloudWatch, SageMaker Model Monitor, MLflow, Langfuse or equivalent.
Understanding of LLM evaluation techniques, such as groundedness and faithfulness metrics, LLM-as-judge and red-teaming.
Familiarity with Amazon Bedrock features such as Guardrails, Model Evaluation and model invocation logging.
Agentic AI
Understanding of agentic AI risks and controls, such as excessive agency, prompt injection, tool misuse and human-in-the-loop design, and of agent evaluation and tracing tools (e.g. Langfuse, OpenTelemetry).
DevOps
Experience with CI/CD pipelines and version control systems such as Git.
Knowledge of container technologies such as Docker and Kubernetes.
Security, privacy & government policy
Good understanding of data security, privacy and Responsible AI principles.
Understanding of Government ICT&SS management policies (IM8), SSP requirements and observability practices, and has applied them to AI guardrails, audit logging and monitoring.
Nice to have
Familiarity with Singapore's Model AI Governance Framework, AI Verify, IM8 and PDPA.
Experience with Government Commercial Cloud (GCC) or security assessments for government projects.
AWS certifications such as Machine Learning Engineer Associate, AI Practitioner or Cloud Practitioner.
Personal attributes
Strong attention to detail and a risk-aware, quality-driven mindset.
Comfortable with Agile methodologies and working closely with product development teams.
Strong on collaboration and communication, including documentation.
Established in 2010, Vinova is a passion-driven and award-winning development company for mobile, web, and enterprise applications. Our clients span the globe and cover a wide range of projects including IoT, blockchain, banking, fintech, networking, and ecommerce.
Vinova earns respect from customers by being committed to quality, flexibility, and speed. We strive to build great products by working with a passion, working closely with our customers, and through open but disciplined collaboration.
You will be working in an encouraging and challenging environment, taking advantage of the latest technology to develop excellent mobile apps for clients. You will be engaging clients in both on-site and off-site support.
Interested to join our all-star team? Submit your application to us now! Tell us why you want to work with us and why you think you’d be a good candidate. We are looking forward to hearing from you!
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