AI Solutions Engineer, GenAI Applications, Global Business …, Toa Payoh

Job description
We are seeking an AI Solutions Engineer with a passion for Generative AI to contribute to the development and operationalization of our Generative AI solution. This full-stack solution will be built directly upon Google Play Partnerships' critical data assets and integrated into our core business consultation workflows. In this role, you will have an opportunity to work closely with mobile games and apps developers, translating data into innovative AI-driven products that directly enable the growth of their businesses. Qualifications
Job responsibilities
Design, build and maintain secure data pipelines Extract, Transform, Load (ETL) required for GenAI and ML models, integrate and prepare various datasets for model training and serving. Implement components of full-stack Generative AI applications, focusing on data-centric techniques such as RAG and Fine-tuning. Develop and maintain Large Language Models (LLM)-based agents, including configuring RAG workflows and utilizing advanced prompt engineering techniques like Multi-hop Chain of Thought Prompting (MCP). Adhere to MLOps standard procedures for deploying, monitoring, and maintaining Generative AI models and data systems in production environments, ensuring performance and reliability. Collaborate with data scientists, consultants, and business stakeholders to implement production-ready solutions. Minimum qualifications
Bachelor's degree or equivalent practical experience. 3 years of experience troubleshooting technical issues for internal/external partners or customers. Experience in either system design or reading code (e.g., Java, C++, Python). Preferred qualifications
6 years of experience writing and maintaining ETL pipelines operating on a variety of structured and unstructured data sources. Experience applying Generative AI technologies to enterprise-scale products and solutions, within a quantitative domain. (Google Agent Development Kit (ADK), Lang-Chain etc. RAG). Understanding of MLOperations/LLMOperations practices for productionizing AI agents and models and Knowledge of cloud-native platforms (e.g., Google Cloud/GCP). Ability to break down ambiguous problems and propose solutions through data modeling and system design. Excellent communication skills to communicate technical concepts to non-technical stakeholders.