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# Generative AI Engineer

- **Company:** Sibitalent Corp
- **Location:** Florham Park, NJ, United States
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Microsoft Azure, Continuous Integration, Data Cleansing, Django Web Framework, Python (Programming Language), NumPy, Cloud Services, Software Deployment, Enterprise Software Applications, Flask (Web Framework), Large Language Models, Prompt Engineering, Model Validation, Generative AI, Git, Fastapi, Pandas, Build Management, Scikit Learn, HuggingFace, Machine Learning Operations, Virtual Agents, Restful APIs, Docker, Microservices
- **Published:** August 7, 2026
- **Apply:** https://www.disabledperson.com/jobs/74049169-generative-ai-engineer

## About the Role

* Experience Required: 5+ years in AI/ML
 * GenAI Experience: Minimum 2 years (hands on)
 * Agentic AI Experience: Minimum 6 months (CrewAI / AutoGen / LangGraph / LangChain Agents), * 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.

## Description

* 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.


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