Data Scientist -- NLP, Deep Learning, GenAI (10 Years)

Key Responsibilities

• Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines.
• Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.).
• Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies.
• Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures.
• Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred).
• Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions.
• Monitor model performance, conduct error analysis, and continuously optimize pipelines.

 

Required Skills

• 10+ years of experience in data science with deep hands‑on expertise in NLP and Generative AI.
• Proficient in transformer models, embeddings, and modern NLP libraries (Hugging Face, spaCy, NLTK).
• Strong Python skills with experience in PyTorch/TensorFlow for advanced model development.
• Practical experience building RAG architectures, vector search, and prompt optimization.
• Solid understanding of MLOps, model deployment, monitoring, and productionization.
• Strong problem‑solving abilities with excellent communication and stakeholder engagement skills.

Key ResponsibilitiesDevelop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines.Build, fine‑tune, and evaluate LLM-based models using transformer architec...

Key Responsibilities

• Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines.
• Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.).
• Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies.
• Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures.
• Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred).
• Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions.
• Monitor model performance, conduct error analysis, and continuously optimize pipelines.

 

Required Skills

• 10+ years of experience in data science with deep hands‑on expertise in NLP and Generative AI.
• Proficient in transformer models, embeddings, and modern NLP libraries (Hugging Face, spaCy, NLTK).
• Strong Python skills with experience in PyTorch/TensorFlow for advanced model development.
• Practical experience building RAG architectures, vector search, and prompt optimization.
• Solid understanding of MLOps, model deployment, monitoring, and productionization.
• Strong problem‑solving abilities with excellent communication and stakeholder engagement skills.

Key ResponsibilitiesDevelop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines.Build, fine‑tune, and evaluate LLM-based models using transformer architec...