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CIMB

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Vice President, GT-DPP, Shared Service Interface MY (Senior Software Engineer - AI Solutions)

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Summary

Senior software engineer (VP grade) at CIMB building enterprise-grade AI solutions for banking domains — conversational AI, RAG, agentic AI, intelligent document processing, and workflow automation. Day-to-day is backend/API development in Python (Java/Node.js desirable), React/Vue/Angular frontends, SQL/NoSQL and vector databases, deployed via Docker/Kubernetes and CI/CD.

Job Purpose *
We are looking for an experienced Senior Software Engineer to design, develop, and deliver enterprise-grade AI solutions for different Banking Domains.
You will work on a diverse range of innovative solutions including conversational AI, Retrieval-Augmented Generation (RAG) platforms, AI agents/Agentic AI, intelligent document processing, workflow automation, knowledge management systems, and enterprise integrations.
Working closely with Solution Architects, and Technical Leads. You will build scalable, secure, and production-ready applications that solve real business challenges while leveraging modern cloud and AI technologies.
Jo Description *
Enterprise AI solution delivery
• Translate approved business use cases and solution designs into secure, scalable and maintainable enterprise applications.
• Develop backend services, microservices, RESTful APIs and, where required, responsive web interfaces using the approved technology stack.
• Build and enhance conversational AI, RAG, semantic search, AI-agent, intelligent document processing, knowledge management and workflow automation capabilities.

Integration, data and security
• Integrate applications with enterprise data services, enterprise databases, vector stores, model platforms, event or messaging services, identity platforms and third-party APIs.
• Implement enterprise controls for authentication and authorisation, secrets, encryption, sensitive-data handling, audit logging, AI guardrails and secure software development.

Engineering quality and operations
• Create automated tests and AI evaluation checks; monitor quality, performance, latency, availability, safety and cost-related engineering indicators.
• Package and deploy applications through approved CI/CD pipelines, containers and Kubernetes-based environments across development, testing and production.
• Troubleshoot defects and production incidents, complete root-cause analysis and implement durable corrective actions within agreed service and delivery timelines.
Collaboration and continuous improvement
• Participate in architecture and technical-design reviews, document solutions, review code and contribute to reusable engineering standards and components.
• Work closely with Product Owners, Solution Architects, Data Scientists, Cybersecurity, Data Governance, DevOps, Infrastructure, UI/UX, business teams and delivery partners; evaluate emerging technologies where they provide measurable business value.

Job Specification *
Qualifications
(Basic Degree/Diploma etc) Bachelor’s Degree in Computer Science / Information Technology or equivalent.
Professional Qualification and/or Regulatory, Licensing requirements Relevant certification in Generative AI/Agentic AI , cloud platforms (Alibaba Cloud, Azure or AWS), Kubernetes, DevSecOps, secure software development, data engineering is advantageous.
Relevant Work Experience • Minimum 5 years of professional software engineering experience, including delivery and support of production applications.
• At least 2 years of hands-on experience with Generative AI, RAG, machine learning or intelligent automation, delivery of at least multiple production-grade AI application is highly desirable.
• Strong hands-on proficiency in Python. Experience with Java and Node.js for enterprise integration is desirable.
• Experience with modern web development using React, Vue.js or Angular.
• Strong understanding of application architecture, object-oriented programming, design patterns, clean code, RESTful APIs and microservices.
• Hands-on experience with SQL, NoSQL and vector databases, Git-based development, automated testing, Docker, Kubernetes or containerised development, CI/CD and Agile delivery.
• Practical experience with RAG pipelines, AI agents, LLM orchestration frameworks, LLM evaluation, observability, security controls and production troubleshooting is highly desirable.

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