What are the responsibilities and job description for the Data Scientist Deep Learning, MLOps & Generative AI position at Infinity Tech Group Inc?
Job Details
Job Title: Data Scientist Deep Learning, MLOps & Generative AI
Location: Weehawken, NJ (Hybrid-3 days)
Duration: 12 Months
Job Summary:
We are seeking an experienced and versatile Data Scientist with a strong foundation in Deep Learning, Machine Learning Ops (MLOps), and Cloud Computing, who also brings hands-on experience in Generative AI and Large Language Models (LLMs) such as ChatGPT, LLaMA, DeepSeek, and Vertex AI.
This role is ideal for someone who can build intelligent systems end-to-end from data preprocessing and model development to deployment while integrating the latest advancements in NLP and generative models.
Key Responsibilities:
- Develop and deploy machine learning models using Deep Learning, NLP, and Generative AI techniques.
- Build and optimize LLM-driven applications (e.g., ChatGPT, LLaMA, DeepSeek, Vertex AI).
- Design and maintain MLOps pipelines for automated training, testing, and model deployment using cloud-native tools.
- Work with large-scale datasets stored in structured and unstructured formats (e.g., MongoDB, SQL, S3).
- Leverage vector embeddings and transformer models for text classification, summarization, question answering, and generative tasks.
- Collaborate with data engineers, product teams, and AI researchers to bring models from prototype to production.
- Monitor model performance in production, retrain models, and manage model versioning.
Required Skills & Qualifications:
- Bachelor s or Master s degree in Data Science, Computer Science, Statistics, or a related field.
- 5 years of experience in machine learning and data science with production-grade models.
- Solid experience in Deep Learning using TensorFlow, PyTorch, or Keras.
- Expertise in NLP and LLMs (e.g., OpenAI GPT, LLaMA, DeepSeek, Vertex AI).
- Strong Python programming skills, with familiarity in libraries like Hugging Face, LangChain, Scikit-learn.
- Experience with cloud platforms: AWS (SageMaker), Google Cloud Platform (Vertex AI), or Azure (ML Studio).
- Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, etc.).
- Experience with NoSQL/SQL databases (e.g., MongoDB, PostgreSQL) and data pipelines.
Preferred Skills:
- Familiarity with RAG (Retrieval-Augmented Generation) and vector databases (FAISS, Pinecone, Weaviate).
- Experience building custom GPT applications or LLM fine-tuning.
- Exposure to real-time inferencing and scalable AI infrastructure.
- Contributions to research, open-source projects, or published papers in AI/ML.
