Agentic AI Developer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an "Agentic AI Developer" in Dallas, TX, on a long-term contract. Key skills include expertise in LangChain and AI system development, with a focus on LLM integration and multi-agent architectures.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
-
🗓️ - Date discovered
August 9, 2025
🕒 - Project duration
Unknown
-
🏝️ - Location type
On-site
-
📄 - Contract type
Unknown
-
🔒 - Security clearance
Unknown
-
📍 - Location detailed
Dallas, TX
-
🧠 - Skills detailed
#AI (Artificial Intelligence) #Langchain #Databases #Strategy
Role description
Agentic AI Developer
Dallas TX
Longterm Contract
• Deep experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom-built agentic stacks.
• Proven ability to build production-grade AI systems not just demos.
• Design, build, and deploy LLM-powered autonomous agents capable of reasoning, tool use, and long-horizon goal execution.
• Architect multi-agent systems that work in coordination (think swarms, workflows, or parallelized task execution).
• Integrate LLMs (GPT-4, Claude, Mistral, Gemini, etc.) with external tools, APIs, databases, and reasoning engines.
• Implement memory modules, retrieval-augmented generation (RAG), dynamic prompt orchestration, and self-evaluation protocols.
• Drive PoCs to production-grade systems with real-world constraints: latency, auditability, scale, and failure tolerance.
• Collaborate closely with our AI Strategy and Platform teams to define reusable patterns, guardrails, and agent behaviors.
Agentic AI Developer
Dallas TX
Longterm Contract
• Deep experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom-built agentic stacks.
• Proven ability to build production-grade AI systems not just demos.
• Design, build, and deploy LLM-powered autonomous agents capable of reasoning, tool use, and long-horizon goal execution.
• Architect multi-agent systems that work in coordination (think swarms, workflows, or parallelized task execution).
• Integrate LLMs (GPT-4, Claude, Mistral, Gemini, etc.) with external tools, APIs, databases, and reasoning engines.
• Implement memory modules, retrieval-augmented generation (RAG), dynamic prompt orchestration, and self-evaluation protocols.
• Drive PoCs to production-grade systems with real-world constraints: latency, auditability, scale, and failure tolerance.
• Collaborate closely with our AI Strategy and Platform teams to define reusable patterns, guardrails, and agent behaviors.
