Technical Solutions Consultant, Agent Assist, Applied AI

Minimum qualifications:

• Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
  
• 3 years of experience in technical project management, stakeholder management, solution development, or technical consulting.
• Experience with LLMs, prompt development, and conversational AI frameworks (e.g., Vertex AI Agent Space, CX Insights).
• Experience programming in one or more languages (e.g., Java, Python, Go, C++).
• Experience with data systems, SQL, data architecture, and data processing within environments like BigQuery.

Preferred qualifications:

• Knowledge of advanced data redaction strategies using Cloud data loss prevention (DLP) API to ensure personally identifiable information (PII) protection and compliance within AI-generated transcripts.
• Understanding of frameworks like LangChain and BigQuery while translating LLM concepts, such as pre-training versus RAG, for non-technical stakeholders.
• Ability integrate LLMs with mission-critical platforms like Salesforce, ServiceNow, and Genesys to create a seamless, reliable "Agent OS" for autonomous operations.
• Ability to maintain a portfolio of open-source projects, internal tools, or technical guides.
• Ability to build production systems within multi-stakeholder technical projects, leveraging Google Cloud foundations as a significant advantage for global deployments.

About the job:

The Cloud Applied AI (AAI) powers business growth with Gemini Enterprise. Our portfolio includes Gemini Enterprise for customer experience (CX), along with other vertical and domain packaged solutions. We enable high adoption and speed to value by building solutions that are quickly deployed, delivering new 0-to-1 capabilities with startup agility. Team members operate at the forefront of AI, collaborating directly with model builders with unprecedented speed. Join us to work on cutting-edge projects and shape the future of AI in a fast-paced, collaborative, and impactful environment.

Responsibilities:

• Drive 0-to-1 technical engagements, designing agent reasoning and guardrails while personally refining retrieval-augmented generation (RAG) pipelines, prompt chains, and application programming interface (API) integrations.
• Design evaluation pipelines using "gold datasets" and automated "judge" LLM frameworks to benchmark latency, accuracy, and brand fidelity.
• Bridge non-deterministic LLM outputs with deterministic systems like Salesforce and ServiceNow to ensure safe, reliable, and mission-critical operations.
• Deploy and stress-test pre-GA features in real-world environments, providing field intelligence to influence Google cloud product and development roadmaps.
• Author architectural patterns and prompt development guides while implementing ingestion pipelines for conversational data using Gemini and Vertex AI.