Data Scientist (GEN AI / Agentic AI)

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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 Python programming with strong understanding of ML pipelines.
• 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).
• Strong problem-solving and communication skills.
• Ability learn new tools, to work independently and collaboratively in a fast-paced environment
• Attention to detail and accuracy Preferred Skills.
• Theoretical knowledge of or experience in Data Engineering, Data Science, AI, ML, RPA or other related domains.
• Certification in Business Analysis or Project Management from a recognized institution.
• Experience in working with agile methodologies such as Scrum or Kanban
  

• 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, FastAPI, APIs, Docker containerisation, Kubeflow. Familiarity with Lang Chain, LlamaIndex, vLLM, HuggingFace Transformers, LoRA and also basic understanding of cost to performance tradeoffs