Role-Agentic AI Engineer Contract -Long term Contract Location-Charlotte, NC Hybrid Key Responsibilities: • Design, build, and deploy RAG pipelines using vector databases and LLMs (e.g., OpenAI, Mistral, Claude, LLaMA, etc.) • Develop intelligent AI agents that can reason, plan, retrieve knowledge, and take actions based on goals • Integrate LLMs with external data sources (e.g., Elasticsearch, Pinecone, Weaviate, LangChain) • Implement tools for document ingestion, chunking, embedding, and indexing • Build API services around AI agents for production use • Fine-tune and evaluate performance of models using feedback loops and A/B testing • Collaborate with data scientists, ML engineers, and DevOps teams for end-to-end deployment • Stay current with advancements in RAG, multi-agent systems, and open-source AI frameworks Required Qualifications: • Bachelor's or Master's degree in Computer Science, AI, ML, or related field • 3–5+ years of experience in machine learning, NLP, or AI engineering • Strong programming skills in Python, experience with LangChain, LLamaIndex, or Haystack • Familiarity with LLMs (e.g., GPT-4, Claude, Mistral, etc.) and embedding models (e.g., OpenAI, Hugging Face) • Hands-on experience with vector databases (e.g., Pinecone, FAISS, Weaviate) • Experience developing RAG-based architectures and real-time document retrieval systems • Proficient in using RESTful APIs, Docker, and Cloud platforms (AWS, GCP, or Azure) • Solid understanding of NLP concepts: tokenization, embeddings, transformers, prompt engineering Preferred Qualifications: • Experience building multi-agent systems or autonomous AI agents • Knowledge of graph databases, knowledge graphs, or ontologies • Prior experience in productionizing GenAI products • Familiarity with LangGraph, AutoGen, CrewAI, or similar agent orchestration frameworks Contributions to open-source AI/ML projects are a plus