Codebase Inc
Agentic AI Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Agentic AI Engineer, a 6-month hybrid contract based in Basking Ridge, NJ, and Irving, TX. Key skills include LangGraph, Python, LLM orchestration, and reinforcement learning. Experience in enterprise AI applications is required. Pay rate: "$X/hour".
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 15, 2025
🕒 - Duration
Unknown
-
🏝️ - Location
Hybrid
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📄 - Contract
Unknown
-
🔒 - Security
Unknown
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📍 - Location detailed
Basking Ridge, NJ
-
🧠 - Skills detailed
#AI (Artificial Intelligence) #Langchain #Scala #Deployment #React #Databases #Python #Automation #Neo4J #Reinforcement Learning #GCP (Google Cloud Platform) #Strategy #Knowledge Graph
Role description
Position : Agentic AI Engineer
Location : Basking Ridge NJ and Irving TX- 3days Hybrid
Primary - LangGraph, ReAct, LangChain, LlamaIndex, Python
Secondary - GCP, Google Spanner/Neo4j, CrewAI, AutoGen, OpenAI
JD:
The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities.
The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Key Responsibilities:
1. Architecting & Scaling Agentic AI Solutions
• Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving.
• Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains.
• Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications.
1. Hands-On Development & Optimization
Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability.
• Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning.
• Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making.
1. Driving AI Innovation & Research
· Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents.
· Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions.
· Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops.
1. AI Strategy & Business Impact
· Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings.
· Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production.
Position : Agentic AI Engineer
Location : Basking Ridge NJ and Irving TX- 3days Hybrid
Primary - LangGraph, ReAct, LangChain, LlamaIndex, Python
Secondary - GCP, Google Spanner/Neo4j, CrewAI, AutoGen, OpenAI
JD:
The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities.
The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Key Responsibilities:
1. Architecting & Scaling Agentic AI Solutions
• Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving.
• Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains.
• Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications.
1. Hands-On Development & Optimization
Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability.
• Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning.
• Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making.
1. Driving AI Innovation & Research
· Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents.
· Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions.
· Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops.
1. AI Strategy & Business Impact
· Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings.
· Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production.
