GenAI & Agentic AI Engineer
Posted Updated
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 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.
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 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.
Expertise You'll Bring:
- 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.
Flexible work from home options available.
Skills
- Agentic AI
- AI
- API
- AutoGen
- Automation
- AWS
- Azure
- CI/CD
- Cloud
- CrewAI
- Django
- Docker
- Embeddings
- FAISS
- FastAPI
- Flask
- GCP
- Generative AI
- Git
- Hugging Face
- LangChain
- LangGraph
- LLM
- Machine Learning
- Microservices
- Model Deployment
- Model Evaluation
- NumPy
- OpenAI
- pandas
- Pinecone
- Prompt Engineering
- Python
- RAG
- REST
- scikit-learn
- Semantic Search
- Vector Databases
- Vector Search
- Weaviate