ประสบการณ์:

1 ปีขึ้นไป

ทักษะ:

Risk Management

ประเภทงาน:

งานประจำ

• Develop and maintain technology risk management policies, standards and processes.
• Communicate technology risk management policies, processes and standards to all relevant parties and advise them on adherence to the same.
• Operate and manage all technology & information security (IT & IS) risk management activities include exception mechanisms, scope for DataX organization and every service which DataX provides to SCBX group companies.
• Work closely with various stakeholders across the organization to ensure a cohesive approach to technology risk management. Assist, challenge and monitor risk owners in applying technology risk management tools, and provide guidance on necessary mitigation measures.
• Collaborate closely with the SCBX Technology Risk team to ensure that the implementation of technology risk management policies, standards, and processes is fully aligned with the group's strategic direction and governance approach.
• Ensure IT & IS control catalogue is defined, implemented and complied with SCBX group/regulatory requirements and international best practices.
• Implement continuous monitoring of IT & IS risks and controls. Review risk assessment, risk mitigation plans and support in structure and escalation.
• Regularly review IT & IS risk profile to address new and evolving threats. Develop and update a set of technology leading risk indicators to assist in mitigating future technology risks.
• Regular review of IT third party risks to ensure that existing third parties remain compliant. Perform evaluation of third parties IT & IS risk posture, to ensure all third parties adhere to the IT & IS requirements and controls.
• Work closely with project owners to perform effectiveness of IT Project Risk management. Assist stakeholders in validating risk assessments, which comprise analyzing, identifying, describing, and quantifying risks that impact all business risks.
• Ensure all technology risk activities are conducted in the GRC tool as centralized repository. Perform analysis of technology risk metrics for emerging risk trends and proactively work with business/support units to address the emerging risks.
• Join the related meetings if need, such as Change Advisory Board (CAB), Technology Steering Committee (TSC) and Risk Management Committee (RMC).
• Regular report technology risk activities, risk profiles and incidents to senior management and/or risk management committee (RMC) to ensure all technology/security-related risks are effectively managed.
• Lead the development, management and implementation of IT & IS literacy program to raise awareness and promote IT & IS risk culture within DataX and measure its effectiveness.
• Respond to internal/external audit programs, findings and coordinate remediation planning for related IT & IS activities to mitigate the risks.
• Manages technology risk team by developing strategies, deploying skilled personnel, providing training, ensuring compliance, and continuously improving practices. Ensure the team, individuals, have the necessary skills and knowledge to effectively manage and mitigate technology risks.
• Regular reviews and updates the framework to address emerging risks and regulatory changes. Coordinate regulatory reviews of technology within DataX and work closely with compliance team, management team and stakeholders, to provide periodic updates on initiatives to meet regulatory commitments and internal policies and standards related to technology.
• Encourage feedback across all related functions in DataX on risk management practices and use this feedback to drive continuous improvement in risk management processes.
• Bachelor's degree or higher in Information Technology, Cybersecurity, Risk Management, or a related field.
• Relevant work experience at least 12+ years of experience in technology risk management, with a minimum of 5 years in any technology role and minimum of 3 years in a leadership role.
• Proficiency in identifying, evaluating, and mitigating technology risks.
• Knowledge of regulatory requirements such as BOT or SEC, and best practices in IT governance.
• Familiarity with risk management frameworks and tools, such as NIST, ISO 27001, and COBIT.
• Strong leadership skills to manage and guide technology risk management in the team and across organization.
• Ability to effectively communicate risk-related information to stakeholders at all levels.
• Commitment to staying updated with the latest trends and developments in technology risk management.
• Strong sense of ethics and integrity in handling sensitive information and making decisions.
• Candidates who are bilingual in Thai and English are preferred.

ทักษะ:

Automation

ประเภทงาน:

งานประจำ

• Strong Python programming skills with experience in production-grade development practices, including writing maintainable, scalable, and well-structured code. Knowledge of TypeScript or Go is a plus.
• Developer mindset with solid understanding of backend development, microservices architecture, and RESTful API design.
• Experience building automation workflows and data pipelines using Python, ETL processes, Bash scripting, and scheduled jobs (e.g., CronJobs).
• Hands-on experience with cloud platforms (Azure) including Azure AI Services, Azure AI Foundry, and related AI infrastructure.
• Experience with containerization and orchestration technologies, such as Docker and Kubernetes, for deploying scalable AI services.
• Good understanding of Agentic AI architectures, AI Agents, and modern LLM frameworks such as LangChain, LangGraph, and AI SDK.
• Ability to design and implement AI agents using: Pro-code frameworks (e.g., Agent SDK), Low-code / no-code solutions (e.g., Azure AI Foundry Agents).
• AI Observability & Monitoring.
• Experience implementing AI observability and monitoring systems for LLM-based applications and AI agents.
• Hands-on experience with AI observability tools such as LangFuse, and logging platforms such as Azure Log Analytics.
• Experience building monitoring dashboards and system telemetry using Grafana.
• Strong understanding of AI system logging, tracing, and performance monitoring for production AI systems.
• Familiarity with LLM evaluation techniques, including LLM-as-a-Judge frameworks to measure agent quality and response performance.
• Understanding of AI evaluation metrics, including model accuracy and response quality latency and throughput, reliability and system health and token usage and cost efficiency.
• Operational Skills.
• Experience with monitoring and observability platforms such as Prometheus, Grafana, and ELK Stack.
• Ability to design operational dashboards to track AI agent KPIs, system health, and service reliability.
• Understanding of model drift detection, data quality monitoring, and AI system observability practices.
• Strong communication and collaboration skills to work with AI engineers, risk teams, product teams, and business stakeholders.
• Design and implement AI observability frameworks to monitor the performance, reliability, and behavior of AI agents and LLM-based systems in production environments.
• Integrate AI observability platforms with third-party systems (e.g., SAS solutions) for governance, compliance monitoring, and operational reporting, using ETL pipelines and data integration workflows.
• Build predictive analytics and automation frameworks to support AI system operations and operational decision-making.
• Develop real-time monitoring dashboards and operational analytics tools for AI systems, including capabilities such as: anomaly detection, predictive forecasting, incident monitoring and alerting root cause analysis for system failures or abnormal agent behavior.
• Define and monitor AI agent KPIs and performance metrics, including quality evaluation using LLM-as-a-Judge approaches.
• Collaborate with AI Engineers, AI Scientists, and Risk teams to define: experiment metrics, evaluation frameworks, continuous monitoring strategies for AI systems.
• Work closely with Product teams, customers, and stakeholders to define business-level KPIs for AI agents and measure their impact on business outcomes.
• Feed operational insights, monitoring results, and evaluation metrics back into the AI development lifecycle to drive continuous improvement of models and agents.
• Manage AI platform operations, including platform upgrades, governance compliance, and SLA monitoring for production AI services.
• Design and maintain data pipelines and operational data infrastructure to ensure standardized, clean, and reliable data for analytics, monitoring, and reporting.
• Collaborate with DevOps, SRE, and IT teams on tooling, infrastructure, CI/CD pipelines, and deployment processes to ensure reliable AI system delivery.