Senior Data Scientist-Gen AI

About the Role:

Senior Data Scientist

data science, AI research, and engineering

Key Responsibilities:

• Design, develop, and fine-tune

  Generative AI models

  for tasks such as text generation, summarization, code generation, image synthesis, and more.
• Leverage

  large language models (LLMs)

  (e.g., GPT, LLaMA, Claude, Mistral) to solve business problems and enhance user experiences.
• Collaborate with cross-functional teams including product, engineering, and design to translate business needs into AI-driven features.
• Conduct

  data exploration, preprocessing, and feature engineering

  on large, complex datasets.
• Deploy models in production environments using

  ML Ops best practices

  .
• Stay updated on the latest research in generative AI and proactively identify opportunities to adopt state-of-the-art techniques.
• Develop and maintain

  documentation, model cards, and evaluation metrics

  for all models.
• Mentor junior data scientists and contribute to team knowledge sharing.

Required Qualifications:

• 58 years

  of professional experience in

  data science or machine learning

  , with at least

  2 years working on generative AI or LLM-based applications

  .
• Strong programming skills in

  Python

  , including libraries like

  PyTorch, TensorFlow, Hugging Face Transformers, LangChain

  , etc.
• Deep understanding of

  machine learning algorithms

  ,

  NLP techniques

  , and

  deep learning architectures

  .
• Experience deploying models at scale using tools like

  Docker, Kubernetes, MLflow, SageMaker

  , or similar.
• Familiarity with

  prompt engineering

  ,

  fine-tuning

  , and

  retrieval-augmented generation (RAG)

  .
• Experience with

  cloud platforms

  such as AWS, Azure, or GCP.

Preferred Qualifications:

• Master's or PhD in Computer Science, Data Science, AI, or related fields.
• Experience working with

  vector databases (e.g., FAISS, Pinecone, Weaviate)

  .
• Publications or contributions to open-source AI/ML projects.
• Understanding of

  ethical AI

  , model bias, and explainability techniques.