Data Scientist

Data Scientist (Deep Learning, NLP & GenAI) (3-5 Years )Experience: 3-5 Years

Industry: Finance/Fintech domain experience is a plus

About the Role:

We are seeking a highly skilled Deep Learning, NLP, and Generative AI Engineer with 3-5 years of hands-on experience in building and optimizing AI models. The ideal candidate will have a strong foundation in deep learning techniques, NLP applications, and generative AI, along with a passion for solving complex problems using cutting-edge AI technologies. While experience in the finance or fintech domain is a plus, it is not mandatory.

Key Responsibilities:

• Design, develop, and deploy deep learning and NLP models for real-world applications.
• Build, fine-tune, and optimize large language models (LLMs) and transformer-based architectures (BERT, GPT, T5, LLaMA).
• Develop and implement generative AI solutions, including text generation, summarization, conversational AI, and code generation.
• Work with large-scale unstructured text data, leveraging advanced NLP techniques such as Named Entity Recognition (NER), Sentiment Analysis, and Text Classification.
• Develop efficient model training, inference pipelines, and optimize model deployment for scalability and performance.
• Utilize frameworks like TensorFlow, PyTorch, Hugging Face, and LangChain to build production-grade AI applications.
• Collaborate with cross-functional teams to integrate AI solutions into products, ensuring robustness and scalability.
• Conduct model evaluations, A/B testing, and improve AI-driven workflows using prompt engineering and fine-tuning strategies.
• Stay up-to-date with the latest advancements in AI/ML, NLP, and GenAI to drive innovation.

Required Skills & Qualifications:

• Experience: 3-5 years in Deep Learning, NLP, and Generative AI.
• Technical Skills: Python, PyTorch/TensorFlow, Hugging Face Transformers, LangChain, LLMOps.
• Strong expertise in transformer models, embeddings, and attention mechanisms.
• Proficiency in handling large-scale text data, vector databases ,and RAG techniques.
• Experience with model fine-tuning, reinforcement learning, and parameter-efficient tuning.
• Knowledge of MLOps practices, model monitoring, and scalable AI deployments using cloud platforms (AWS/GCP).
• Familiarity with financial/fintech applications (nice to have but not mandatory).

Preferred Qualifications:

• Master’s or Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related field.