Role description- Job Title: Senior AI Engineer Agentic Systems Enterprise GCP
- Location: Southlake, TX (3 days Hybrid)
- Employment: Type FullTime
Job SummaryWe 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
Required Technical Skills- Core Programming Strong mastery of Python and standard enterprise software design patterns
- Agentic Orchestration Deep handson experience building complex productionready agentic workflows with LangChain LangGraph CrewAI or Googles native Agent Development Kit ADK
- Google Cloud GCP Proven expertise with Gemini Enterprise Agent Platform Vertex AI Agent Builder GKE Google Kubernetes Engine Cloud Run and IAM
- Protocols Standards Familiarity with modern agentic communication standards like Model Context Protocol MCP and Agent2Agent A2A protocols
- Databases Vector Search Strong understanding of Vertex AI Vector Search or standalone vector databases for semantic search metadata filtering and embedding lifecycle management
- DevOps MLOps Proficiency with modern CICD tools eg GitHub Actions GitLab CI and containerization DockerKubernetes
Qualifications Experience- Experience 5 years of software engineering experience with at least 23 years dedicated to building and deploying AILLMpowered applications and agentic systems at an enterprise scale
- Education Bachelors or Masters degree in Computer Science AI Data Science or a related technical field or equivalent practical experience
