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AIML | JAVA
AIML | JAVAeTeam • Chicago, IL
AIML | JAVA

AIML | JAVA

eTeam • Chicago, IL
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Urgent Hiring : AI / ML Engineer (Java / Python - Azure AI & RAG Specialist)

Location : Weehawken, NJ (Onsite)

Job Description :

We are seeking a highly skilled and innovative AI / ML Engineer with strong experience in Java, Python , and advanced AI / ML frameworks and cloud services . The ideal candidate will have a deep understanding of building intelligent systems using tools such as Spring AI, Langchain, LangGraph, Agno, and TensorFlow , along with hands-on expertise in Azure AI services , including Azure OpenAI and Azure AI Search .

This role is ideal for someone passionate about building real-world AI solutions, especially around RAG (Retrieval-Augmented Generation) and agent-based architectures .

Key Responsibilities :

  • Design and develop AI-driven applications using Java (Spring AI) and Python .
  • Implement and optimize solutions using Langchain, LangGraph, Agno , and TensorFlow .
  • Develop and deploy intelligent agents and RAG pipelines , including Agentic RAG , functional calling , and context engineering .
  • Work extensively with Azure OpenAI , Azure AI Search , and other Azure cloud services for AI workloads.
  • Engineer effective prompts, optimize embeddings / chunking strategies, and enable real-time data integration.
  • Utilize vector databases (, FAISS, Pinecone, Weaviate) for embedding storage and similarity search.
  • Collaborate with data scientists, product managers, and engineering teams to deliver scalable AI solutions.
  • Maintain clean, testable, and well-documented code and pipelines.

Required Skills :

  • Strong programming skills in Java and Python .
  • Experience with modern AI / ML frameworks : Spring AI, Langchain, LangGraph, Agno, TensorFlow .
  • Hands-on experience with Azure AI services , including : Azure OpenAI Azure AI Search
  • Proficiency in : RAG (Retrieval-Augmented Generation) Agentic RAG Function calling Prompt and context engineering Real-time data integration Embedding and chunking strategies
  • Experience with vector databases for similarity search and indexing.
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