Data Scientist

Key Responsibilities:

·      Design, build, and deploy scalable NLP/ML models for real-world applications.

·      Fine-tune and optimize Large Language Models (LLMs) using techniques like LoRA, PEFT, or QLoRA.

·      Work with transformer-based architectures (e.g., BERT, GPT, LLaMA, T5, etc.).

·      Develop GenAI applications using frameworks such as LangChain, Hugging Face, OpenAI API, or RAG (Retrieval-Augmented Generation).

·      Write clean, efficient, and testable Python code.

·      Collaborate with data scientists, software engineers, and stakeholders to define AI- driven solutions.

·      Evaluate model performance and iterate rapidly based on user feedback and metrics.

Required Skills s Qualifications:

·      3+ years of experience in Data Science(GEN AI/Machine learning)

·      Good understanding & Hand on experience in Agentic Ai.

·      Solid understanding and experience in NLP, including text preprocessing, embeddings, NER, sentiment analysis, etc.

·   Proficiency in ML libraries: scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, spaCy.

·      Experience with GenAI concepts, including prompt engineering, LLM fine-tuning, and vector databases (e.g., FAISS, ChromaDB).

·      Ability learns new tools, to work independently and collaboratively in a fast-paced environment & Strong problem-solving and communication skills.

·      Experience in working with Python Programing and agile methodologies such as Scrum or Kanban

Please find the below points for your reference.

·      Experience on Deep Learning and Transformer architectures and models 

·      Prompt Engineering, training LLMs and GenAI pipeline preparation. practical experience in integrating LLM models like ChatGPT, Gemini, Claude etc with context aware capabilities using RAG or fine-tuning models. 

·      Model Evaluation & Alignment. Metrics to calculate the model’s accuracy. 

·      Data Curation from sources for RAG Preprocessing and development LLM Pipelines. 

·      Scalable Deployment and logging tooling. Must have skills like Flask, Django, Fast API, APIs, Docker containerisation, Kubeflow. Familiarity with Lang Chain, LlamaIndex, vLLM, Hugging Face Transformers, LoRA and also basic understanding of cost to performance tradeoffs