

Agentic AI Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for an "Agentic AI Engineer" with a contract length of "unknown" and a pay rate of "unknown." It requires on-site work, expertise in Google Vertex AI, LangChain, and GCP, along with experience in AI system deployment and MLOps practices.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
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ποΈ - Date discovered
September 23, 2025
π - Project duration
Unknown
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ποΈ - Location type
On-site
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π - Contract type
Unknown
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π - Security clearance
Unknown
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π - Location detailed
Auburn Hills, MI
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π§ - Skills detailed
#Deployment #Monitoring #Scala #NLP (Natural Language Processing) #AI (Artificial Intelligence) #Langchain #Databases #GCP (Google Cloud Platform)
Role description
Must come and work on-site from Day 1.
Skills
Key Responsibilities
β’ Design and implement agentic AI architectures using Google Vertex AI models (Gemini, PaLM, Codey)
β’ Develop and deploy AI agents using LangChain and Model Context Protocol (MCP) architectures
β’ Build scalable, production-ready AI systems leveraging GCP services including Vertex AI Pipelines, Matching Engine, and AI Platform Implement multi-agent systems with complex reasoning, planning, and tool-use capabilities
β’ Design and optimize vector databases and embedding systems for retrieval-augmented generation (RAG)
β’ Integrate agents with external APIs, databases, and enterprise systems
β’ Establish MLOps practices for agent deployment, monitoring, and continuous improvement
staffing@techjordan.com
#generativeai #airchitect #LLM #GCP #NLP #deeplearning
Must come and work on-site from Day 1.
Skills
Key Responsibilities
β’ Design and implement agentic AI architectures using Google Vertex AI models (Gemini, PaLM, Codey)
β’ Develop and deploy AI agents using LangChain and Model Context Protocol (MCP) architectures
β’ Build scalable, production-ready AI systems leveraging GCP services including Vertex AI Pipelines, Matching Engine, and AI Platform Implement multi-agent systems with complex reasoning, planning, and tool-use capabilities
β’ Design and optimize vector databases and embedding systems for retrieval-augmented generation (RAG)
β’ Integrate agents with external APIs, databases, and enterprise systems
β’ Establish MLOps practices for agent deployment, monitoring, and continuous improvement
staffing@techjordan.com
#generativeai #airchitect #LLM #GCP #NLP #deeplearning