AI Engineer

Role description

• Job Title: Senior AI Engineer Agentic Systems Enterprise GCP

• Location: Southlake, TX (3 days Hybrid)

• Employment: Type FullTime

Job Summary

We are seeking a highly skilled and forwardthinking Senior AI Engineer to lead the architecture scalability and integration of our enterprise agentic AI platforms In this role you will design and deploy autonomous multiagent workflows capable of executing complex reasoning loops tool usage and crossplatform collaboration

The ideal candidate bridges the gap between cuttingedge agentic frameworks and robust productiongrade software engineering You will leverage Google Clouds Gemini Enterprise Agent Platform formerly Vertex AI to build secure scalable and highly optimized AI pipelines If you have a passion for managing multiagent systems optimizing token consumption implementing Model Context Protocol MCP and building secure enterprise integrations within a strict CICD framework we want you on our team

Key Responsibilities

• Agentic Architecture Application Development Design and build productiongrade Python applications using advanced orchestration frameworks like LangChain LangGraph and CrewAI to manage autonomous multiagent systems stateful reasoning and complex RAG RetrievalAugmented Generation workflows
• GCP Agent Platform Infrastructure Deploy scale and govern AI agents leveraging the full Gemini Enterprise Agent Platform Vertex AI Agent Builder ecosystem This includes utilizing the Agent Development Kit ADK for codefirst deployment Agent Engine for stateful runtimes and Agent Studio for prototyping
• Tool Protocol Integration Enable interagent collaboration and data access by implementing the Agent2Agent A2A protocol and Model Context Protocol MCP connecting agentic workflows securely to enterprise databases remote MCP servers and thirdparty workflow APIs
• AI Engineering Token Optimization Actively monitor profile and optimize LLM prompt structures context windows and caching mechanisms to maximize agent reasoning efficiency while minimizing token consumption and operational costs
• Grounding Knowledge Architecture Design and maintain hybrid search structures using Vertex AI Vector Search and Vertex AI Search to ground agent decisions in authoritative enterprise data local files and external specialized data sources
• Enterprise Security Compliance Architect decentralized agent systems under zerotrust principles Enforce robust data privacy protocols using Vertex AI Model Armor to prevent prompt injections and manage permissions securely via Agent Identity and Google Cloud IAM
• DevOps Agent Observability Establish and maintain robust CICD pipelines to automate the testing versioning and deployment of agentic systems Utilize Vertex AI Agent Engine Runtime tracing logging and Unified Trace Viewers to debug complex agent reasoning loops in production