About the role
AI summarisedAI Solutions Engineer role at Google's Global Business Consulting, focusing on building Generative AI solutions for mobile games and apps developers. Responsibilities include designing data pipelines, implementing full-stack GenAI applications, and collaborating with stakeholders. Requires 3+ years experience in troubleshooting and system design or coding, with preferred experience in ETL pipelines and GenAI technologies.
BusinessFull-timeGeneral
Key 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.
Requirements
- 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).
- 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.