Data Scientist (GEN AI / Agentic AI) SourcingXPress

3.0 - 7.0 years

0 Lacs

hyderabad, telangana

On-site

You will be responsible for designing, building, and deploying scalable NLP/ML models for real-world applications. Your role will involve fine-tuning and optimizing Large Language Models (LLMs) using techniques like LoRA, PEFT, or QLoRA. You will work with transformer-based architectures such as BERT, GPT, LLaMA, and T5, and develop GenAI applications using frameworks like LangChain, Hugging Face, OpenAI API, or RAG (Retrieval-Augmented Generation). Writing clean, efficient, and testable Python code will be a crucial part of your tasks. Collaboration with data scientists, software engineers, and stakeholders to define AI-driven solutions will also be an essential aspect of your work. Additionally, you will evaluate model performance and iterate rapidly based on user feedback and metrics. The ideal candidate should have a minimum of 3 years of experience in Python programming with a strong understanding of ML pipelines. A solid background and experience in NLP, including text preprocessing, embeddings, NER, and sentiment analysis, are required. Proficiency in ML libraries such as scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, and spaCy is essential. Experience with GenAI concepts, including prompt engineering, LLM fine-tuning, and vector databases like FAISS and ChromaDB, will be beneficial. Strong problem-solving and communication skills are highly valued, along with the ability to learn new tools and work both independently and collaboratively in a fast-paced environment. Attention to detail and accuracy is crucial for this role. Preferred skills include theoretical knowledge or experience in Data Engineering, Data Science, AI, ML, RPA, or related domains. Certification in Business Analysis or Project Management from a recognized institution is a plus. Experience in working with agile methodologies such as Scrum or Kanban is desirable. Additional experience in deep learning and transformer architectures and models, prompt engineering, training LLMs, and GenAI pipeline preparation will be advantageous. Practical experience in integrating LLM models like ChatGPT, Gemini, Claude, etc., with context-aware capabilities using RAG or fine-tuning models is a plus. Knowledge of model evaluation and alignment, as well as metrics to calculate model accuracy, is beneficial. Data curation from sources for RAG preprocessing and development of LLM pipelines is an added advantage. Proficiency in scalable deployment and logging tooling, including skills like Flask, Django, FastAPI, APIs, Docker containerization, and Kubeflow, is preferred. Familiarity with Lang Chain, LlamaIndex, vLLM, HuggingFace Transformers, LoRA, and a basic understanding of cost-to-performance tradeoffs will be beneficial for this role.,

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