Job Description

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

• 5–8 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.