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Lead AI Developer / Forward Deployed Engineer
Career Insights for Artificial Intelligence Engineer (General)
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What they do
An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
$160,491 / year median in California
Job Description
Job Title:
Lead Applied AI Developer /
Forward Deployed Engineer Location:
San Jose, CA Duration:
6+
Months Contract Travel:
Up to 50% We are seeking experienced Lead Applied AI Developers / Forward Deployed Engineers to design, develop, and deploy enterprise Conversational AI and Agentic AI solutions using Google Cloud technologies . The ideal candidate will have strong hands-on experience with Google Gemini, Conversational AI, CCAI, Dialogflow CX, Google Cloud Platform, and Python , along with experience taking AI solutions from prototype to production.
Responsibilities:
Design, develop, and deploy enterprise Conversational AI and Agentic AI solutions Utilize Google Gemini, CCAI, and Dialogflow/Dialogflow CX Work with Google Cloud Platform (Google Cloud Platform) to implement AI solutions Develop AI agents and multi-agent workflows Implement ReAct, self-reflection, and agentic architectures Build and maintain enterprise knowledge bases Integrate APIs and microservices Utilize Terraform for Infrastructure as Code Evaluate, trace, monitor, and ensure observability of AI applications Deploy AI applications into production Troubleshoot and debug AI solutions as needed Communicate effectively with customers and stakeholders Travel up to 50% as required
Requirements:
Strong experience with Conversational AI and Agentic AI Hands-on experience with Google Gemini, CCAI, and Dialogflow/Dialogflow CX Strong Google Cloud Platform (Google Cloud Platform) experience 5+ years of professional Python development Experience building AI agents and multi-agent workflows Knowledge of ReAct, self-reflection, and agentic architectures Experience with RAG and enterprise knowledge bases API and microservices integration Terraform / Infrastructure as Code AI evaluation, tracing, monitoring, and observability Experience deploying AI applications into production Strong troubleshooting and debugging skills Excellent communication and customer-facing skills Willingness to travel up to 50%