Generative AI Engineer

Sibitalent Corp; Whippany (NJ); On-site; USD 120,000 - 180,000; Full time

Job summary

Sibitalent Corp is seeking a senior AI engineer to build and deploy GenAI applications using leading LLMs and Agentic AI frameworks. You will design RAG pipelines, enable embedding-based retrieval, and deploy scalable services on Azure/AWS/GCP.

Ideal candidates have 5+ years in AI/ML, 2+ years in Generative AI, and 6+ months with Agentic AI tools, with strong Python and REST API expertise.

Qualifications

• 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.

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.

Skills

GenAI Experience

Agentic AI Experience

Python

LLMs

Prompt Engineering

Web APIs (FastAPI/Flask)

Cloud Deployment

REST & Microservices

Git & CI/CD

Embeddings & Vector Search

Tools

CrewAI

AutoGen

LangGraph

LangChain Agents

FAISS

Pinecone

Chroma

Weaviate

Job description

• GenAI Experience: Minimum 2 years (hands on)
• Agentic AI Experience: Minimum 6 months (CrewAI / AutoGen / LangGraph / 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.