Overview
Hybrid
Depends on Experience
Contract - Independent
Contract - W2
Skills
LLM
Job Details
Job Title : Data Scientist with Gen AI Location : Jersey City, NJ (Hybrid) Locals Only or near by Long Term Contract EXP :-9-20 years of experience
Key Responsibilities
Build and optimize classification, regression, and forecasting models using classical ML and deep learning techniques.
Develop and deploy deep learning architectures including LSTMs, transformers, and other sequence-based models for time-series, NLP, and anomaly detection.
Design and implement NLP pipelines for text classification, semantic search, summarization, and question answering using transformer-based models (e.g., BERT, T5, GPT).
Create RAG (retrieval-augmented generation) pipelines integrating LLMs with vector databases (e.g., FAISS, Pinecone, Weaviate) and document indexing frameworks.
Apply and fine-tune LLMs (e.g., OpenAI, Mistral, LLaMA, Cohere) for domain-specific tasks using supervised fine-tuning or LoRA/QLoRA methods.
Build and orchestrate multi-agent AI systems using frameworks like LangGraph, CrewAI, or OpenAgents to support tool-using, autonomous agents for decision-making workflows.
Collaborate with data engineers, product managers, and stakeholders to translate business needs into production-ready solutions.
Mentor and support junior data scientists through code reviews, model design feedback, and collaborative experimentation.
Promote best practices in reproducible modeling, responsible AI, and scalable deployment.
______________________________
Required Skills & Experience
5+ years of experience in data science or applied machine learning, with a strong background in both classical and deep learning methods.
Hands-on experience with Python, and libraries/frameworks such as scikit-learn, pandas, PyTorch, TensorFlow, Hugging Face Transformers, and LangChain.
Strong understanding of classification metrics, feature engineering, model validation, and hyperparameter tuning.
Demonstrated experience with LLMs, including fine-tuning, prompt engineering, and retrieval-augmented generation techniques.
Familiarity with vector databases, embedding models, and chunking strategies for unstructured data (e.g., PDFs, knowledge bases).
Experience working with multi-agent architectures or orchestration tools like LangGraph, CrewAI, or AutoGPT.
Solid skills in data analysis, visualization, and communicating technical insights clearly to mixed audiences.
Knowledge of cloud platforms (AWS/Google Cloud Platform/Azure) and deployment tools (e.g., Docker, MLflow, FastAPI).
Build and optimize classification, regression, and forecasting models using classical ML and deep learning techniques.
Develop and deploy deep learning architectures including LSTMs, transformers, and other sequence-based models for time-series, NLP, and anomaly detection.
Design and implement NLP pipelines for text classification, semantic search, summarization, and question answering using transformer-based models (e.g., BERT, T5, GPT).
Create RAG (retrieval-augmented generation) pipelines integrating LLMs with vector databases (e.g., FAISS, Pinecone, Weaviate) and document indexing frameworks.
Apply and fine-tune LLMs (e.g., OpenAI, Mistral, LLaMA, Cohere) for domain-specific tasks using supervised fine-tuning or LoRA/QLoRA methods.
Build and orchestrate multi-agent AI systems using frameworks like LangGraph, CrewAI, or OpenAgents to support tool-using, autonomous agents for decision-making workflows.
Collaborate with data engineers, product managers, and stakeholders to translate business needs into production-ready solutions.
Mentor and support junior data scientists through code reviews, model design feedback, and collaborative experimentation.
Promote best practices in reproducible modeling, responsible AI, and scalable deployment.
______________________________
Required Skills & Experience
5+ years of experience in data science or applied machine learning, with a strong background in both classical and deep learning methods.
Hands-on experience with Python, and libraries/frameworks such as scikit-learn, pandas, PyTorch, TensorFlow, Hugging Face Transformers, and LangChain.
Strong understanding of classification metrics, feature engineering, model validation, and hyperparameter tuning.
Demonstrated experience with LLMs, including fine-tuning, prompt engineering, and retrieval-augmented generation techniques.
Familiarity with vector databases, embedding models, and chunking strategies for unstructured data (e.g., PDFs, knowledge bases).
Experience working with multi-agent architectures or orchestration tools like LangGraph, CrewAI, or AutoGPT.
Solid skills in data analysis, visualization, and communicating technical insights clearly to mixed audiences.
Knowledge of cloud platforms (AWS/Google Cloud Platform/Azure) and deployment tools (e.g., Docker, MLflow, FastAPI).
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.