GenAI & Agentic AI Engineer

Job Description

Job Description

Role: GenAI & Agentic AI Engineer
Location: Whippany, NJ (Hybrid)
Hire Type: FTE

* Must be legally authorized to work in US without need for employer sponsorship now or at any time in the future.

About Position:
We are looking for a skilled GenAI & Agentic AI Engineer with strong experience in building endtoend AI/ML solutions, Generative AI applications, and agentbased automation workflows. The ideal candidate will have a solid background in machine learning along with handson expertise in LLMs, RAG, embeddings, vector databases, and Agentic AI frameworks such as CrewAI, AutoGen, LangGraph, or LangChain Agents.

What You'll Do:

• 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 embeddingbased retrieval systems.
• Build scalable AI services using Python , FastAPI/Flask, and cloud platforms (Azure/AWS/GCP).
• Collaborate with crossfunctional teams to define use cases and convert them into productionready GenAI solutions.
• Implement hallucination reduction , promptengineering 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.

Expertise You'll Bring:

• 5+ years of experience in AI/ML, including model development, data preprocessing, EDA, training, and evaluation.
• 2+ years of handson experience in Generative AI (LLMs, embeddings, RAG, LLMbased apps).
• 6+ months of handson experience with Agentic AI frameworks (CrewAI / AutoGen / LangGraph / LangChain Agents).
• Strong proficiency in Python and ML libraries (Scikitlearn, Pandas, NumPy).
• Experience with OpenAI APIs , Azure OpenAI, HuggingFace, and prompt engineering.
• Familiarity with building scalable APIs using FastAPI , Flask, or Django.
• Handson 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.

Flexible work from home options available.