Senior AI Solutions Engineer, Genai Applications, Global Consulting

Google Google · Big Tech · Singapore

This role focuses on building and operationalizing Generative AI solutions, specifically LLM-based agents and applications, for advertising clients. It involves designing data pipelines, implementing RAG and fine-tuning techniques, developing agents with advanced prompting, and adhering to MLOps for production deployment and monitoring. The role requires collaboration with data scientists and business stakeholders to deliver AI-driven products.

What you'd actually do

  1. 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.
  2. Implement components of full-stack Generative AI applications, focusing on data-centric techniques such as RAG and fine-tuning.
  3. 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).
  4. Adhere to MLOps standard procedures for deploying, monitoring, and maintaining Generative AI models and data systems in production environments, ensuring performance and reliability.
  5. Collaborate with data scientists, consultants, and business stakeholders to implement production-ready solutions.

Skills

Required

  • 5 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)

Nice to have

  • 8 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 (e.g., Google Agent Development Kit (ADK), Lang-Chain, RAG, etc.)
  • Understanding of ML Operations/LLM Operations 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

What the JD emphasized

  • Generative AI
  • LLM-based agents
  • RAG
  • fine-tuning
  • MLOps
  • production environments

Other signals

  • Generative AI
  • LLM-based agents
  • RAG
  • fine-tuning
  • MLOps