Promote an AI-first mindset and support business teams in adopting AI tools effectively.
Design, develop, and deploy internal enterprise AI applications based on LLM, RAG, Agent, and workflow automation technologies.
Lead AI application incubation from zero to one, including requirement analysis, solution design, MVP development, testing, iteration, and production deployment.
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Vendor Management & In-House Capability: Oversee external vendor delivery and technical integration in early phases, while laying the foundation to build and scale internal development capabilities.
Governance & Standards: Define enterprise AI governance, model lifecycle management (MLOps), data security, and ethical AI frameworks.
Solid background in AI Architecture, Solution Architecture, or Senior Data Engineering within enterprise environments.
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Apply Practical ML : Implement data analytics, forecasting models, and light machine learning where they materially elevate enterprise decision quality.
Enforce Best Practices : Drive rigorous software engineering standards, including CI/CD, automated testing, version control, infrastructure automation, and clear technical documentation.
Ensure Alignment : Guarantee all data and AI solutions fully comply with group technology standards, security frameworks, and architectural guardrails.
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