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Rivago Infotech Inc Linkedin · Posted 1mo ago

GenAI & Agentic AI Engineer

Whippany, New Jersey, United States

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Indexed description

We are seeking a skilled GenAI & Agentic AI Engineer with strong experience in building end‑to‑end AI/ML solutions, Generative AI applications, and agent‑based automation workflows. The ideal candidate will have a solid background in machine learning along with hands‑on expertise in LLMs, RAG, embeddings, vector databases, and Agentic AI frameworks such as CrewAI, AutoGen, LangGraph, or LangChain Agents.

Key Responsibilities

  • Build and deploy GenAI applications using LLMs (OpenAI, Azure OpenAI, Claude, Gemini, Llama, etc.).
  • Develop Agentic AI workflows using frameworks such as CrewAI, AutoGen, LangGraph, or LangChain Agents.
  • Design and implement RAG pipelines, vector search solutions, and embedding‑based retrieval systems.
  • Build scalable AI services using Python, FastAPI/Flask, and cloud platforms (Azure/AWS/GCP).
  • Collaborate with cross‑functional teams to define use cases and convert them into production‑ready GenAI solutions.
  • Implement hallucination reduction, prompt‑engineering strategies, and model evaluation methods.
  • Integrate LLMs with enterprise applications, APIs, and automation workflows.
  • Work with vector databases (FAISS, Pinecone, Chroma, Weaviate) for semantic search.
  • Monitor, evaluate, and optimize GenAI models for accuracy, performance, and cost.

Required Skills & Experience

  • 5+ years of experience in AI/ML, including model development, data preprocessing, EDA, training, and evaluation.
  • 2+ years of hands‑on experience in Generative AI (LLMs, embeddings, RAG, LLM‑based apps).
  • 6+ months of hands‑on experience with Agentic AI frameworks (CrewAI / AutoGen / LangGraph / LangChain Agents).
  • Strong proficiency in Python and ML libraries (Scikit‑learn, Pandas, NumPy).
  • Experience with OpenAI APIs, Azure OpenAI, HuggingFace, and prompt engineering.
  • Familiarity with building scalable APIs using FastAPI, Flask, or Django.
  • Hands‑on knowledge of cloud services (Azure/AWS/GCP) for AI deployment.
  • Strong understanding of REST APIs, microservices, and integration patterns.
  • Experience with Git, CI/CD, Docker, and model deployment best practices.

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