Responsibilities

Design end-to-end AI solutions that are robust, scalable, and span data ingestion, model deployment, API orchestration, and business system integration across hybrid cloud and on-prem environments
Architect and deliver solutions for the Central AI platform, encompassing Agentic AI, AI Workbench and Tooling, Model Management, shared RAG service, MCP, AI Runtime environments, etc
Develop solution blueprinting by translating business needs into technical architecture, including selecting appropriate tools, frameworks, and infrastructure for AI model development and operations
Integrate AI capabilities with internal APIs, enterprise platforms and user-facing applications in IT and Networks including LLM-based and agentic workflows
Ensure secure and compliant architecture in collaboration with cybersecurity and governance teams, embedding PDPA and enterprise policy requirements into designs
Evaluate and recommend AI platforms and tools (e.g. Portkey, Databricks, open-source toolkits) based on enterprise goals and technical fit
Collaborate with AI engineering and data teams to operationalize AI models, ensuring architectural alignment, scalability, and lifecycle support
Contribute to proof-of-concepts (PoCs), technical evaluations, and prototyping efforts under supervision
Stay current with AI technologies and best practices in integration, model lifecycle management, and platform operations
Participate in architecture reviews, technical discussions, and sprint planning with cross-functional teams.
Define and enforce architectural standards, reusable design patterns, open-standard, and reference implementations to streamline AI deployment across business units
Provide technical leadership in prototyping, experimentation, vendor technology evaluations, and innovation pilots
Stay updated on emerging AI technologies such as vector databases, context-aware agents, orchestration protocols (e.g. LangChain, LangGraph, MCP, A2A, etc) and assess their applicability within the enterprise
Responsible for leading solutioning activities and managing a small development team consists of AI engineer and application developer for selected use cases

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Data, AI/ML, or related field.
3 to 5 years of experience in solution architecture in AI/ML platform integration, data pipeline design and API-driven systems
Proven expertise in designing and integrating end-to-end AI workflows, including data pipelines, model orchestration, and serving APIs across hybrid cloud environments
Experience in designing AI/ML systems, cloud-native architectures, and API ecosystems.
Hands-on experience with cloud-based AI services such as Microsoft Azure ML, AI Foundry, AWS SageMaker, Bedrock, including model deployment, monitoring, and scaling.
Familiar with open-source/open-standard tooling for AI and agentic framework. (e.g. Portkey or equivalent)
Proficient in API architecture and integration standards with experience enabling secure and scalable interfaces between AI models and enterprise system.
Experience with tools and frameworks such as LangChain, LangGraph, GraphRAG, Retrieval-Augmented Generation (RAG), Ray, Kubeflow, and related AI/ML orchestration technologies.
Capable to evaluate and integrate new AI technologies, frameworks, and vendor solutions into enterprise environments
Effective collaboration skills to work across data, API, ML or AI platform teams, with a clear communication style to bridge business and technical stakeholders.
Good internal (IT, Networks, business) and external (suppliers, government) stakeholders management skills
Strong technical writing and presentation skills, with the ability to communicate complex concepts clearly to both technical and non-technical stakeholders.
Proactive and fast learner with a strong drive to stay current on emerging technologies and industry trends.
Familiarity with the telco domain, IT systems, and data-driven use cases, with strong technical acumen and a keen interest in emerging AI and data technologies.