

JKV International
W2 Contract -Agentic AI Lead / Developer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a W2 Contract - Agentic AI Lead / Developer for 6 months in Dallas, TX, and Basking Ridge, NJ. Requires expertise in LangGraph, Python, and LLM orchestration. Strong knowledge of reinforcement learning and AI implementation is essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 24, 2025
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Dallas, TX
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🧠 - Skills detailed
#Knowledge Graph #Strategy #Deployment #Scala #Langchain #React #Databases #Automation #Python #Reinforcement Learning #AI (Artificial Intelligence) #Neo4J #GCP (Google Cloud Platform)
Role description
2 Developer roles – Dallas, TX
1 Lead Developer – Basking Ridge, NJ
Primary Skills:
LangGraph, ReAct, LangChain, LlamaIndex, Python
Secondary Skills:
GCP, Google Spanner / Neo4j, CrewAI, AutoGen, OpenAI
Role Overview – Agentic AI Lead / Developer
The Agentic AI Lead will drive the research, development, and deployment of semi-autonomous AI agents designed to solve complex enterprise challenges. This role requires hands-on expertise with LangGraph and the ability to lead initiatives that build multi-agent systems capable of autonomy, adaptability, and intelligent decision-making.
The ideal candidate should have deep knowledge of LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI implementation.
Key Responsibilities
Architecting & Scaling Agentic AI Solutions
• Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous operations.
• Build memory-augmented, context-aware agents capable of reasoning, planning, and executing tasks across domains.
• Define scalable architectures for LLM-powered agents integrated with enterprise applications.
Hands-On Development & Optimization
• Develop and optimize orchestration workflows using LangGraph with a focus on modularity, scalability, and performance.
• Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and RAG techniques to enhance agent reasoning.
• Apply reinforcement learning (RLHF/RLAIF) to fine-tune AI agents for better decision-making and adaptability.
Driving AI Innovation & Research
• Lead cutting-edge initiatives in Agentic AI, LangGraph, and LLM orchestration.
• Stay ahead of advancements in multi-agent systems, AI planning, and self-improving agents.
• Prototype and experiment with self-learning AI systems that adapt via real-time feedback.
AI Strategy & Business Impact
• Translate Agentic AI capabilities into enterprise-grade solutions for automation, efficiency, and ROI.
• Lead PoCs to validate business impact and scale successful prototypes to production.
2 Developer roles – Dallas, TX
1 Lead Developer – Basking Ridge, NJ
Primary Skills:
LangGraph, ReAct, LangChain, LlamaIndex, Python
Secondary Skills:
GCP, Google Spanner / Neo4j, CrewAI, AutoGen, OpenAI
Role Overview – Agentic AI Lead / Developer
The Agentic AI Lead will drive the research, development, and deployment of semi-autonomous AI agents designed to solve complex enterprise challenges. This role requires hands-on expertise with LangGraph and the ability to lead initiatives that build multi-agent systems capable of autonomy, adaptability, and intelligent decision-making.
The ideal candidate should have deep knowledge of LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI implementation.
Key Responsibilities
Architecting & Scaling Agentic AI Solutions
• Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous operations.
• Build memory-augmented, context-aware agents capable of reasoning, planning, and executing tasks across domains.
• Define scalable architectures for LLM-powered agents integrated with enterprise applications.
Hands-On Development & Optimization
• Develop and optimize orchestration workflows using LangGraph with a focus on modularity, scalability, and performance.
• Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and RAG techniques to enhance agent reasoning.
• Apply reinforcement learning (RLHF/RLAIF) to fine-tune AI agents for better decision-making and adaptability.
Driving AI Innovation & Research
• Lead cutting-edge initiatives in Agentic AI, LangGraph, and LLM orchestration.
• Stay ahead of advancements in multi-agent systems, AI planning, and self-improving agents.
• Prototype and experiment with self-learning AI systems that adapt via real-time feedback.
AI Strategy & Business Impact
• Translate Agentic AI capabilities into enterprise-grade solutions for automation, efficiency, and ROI.
• Lead PoCs to validate business impact and scale successful prototypes to production.






