Data Scientist

We are looking for a skilled and hands-on Data Scientist with 3–8 years of experience in developing and deploying machine learning models—ranging from traditional ML algorithms to advanced deep learning and Generative AI systems. The ideal candidate brings a strong foundation in classification, anomaly detection, and time-series modeling, along with hands-on experience in deploying and optimizing Transformer-based models . Familiarity with quantization , fine-tuning , and RAG (Retrieval-Augmented Generation) is highly desirable.Exp-3-8 YearsMode-RemoteNp-Immediate-15 DaysResponsibilitiesDesign, train, and evaluate ML models for tasks such as classification, anomaly detection, forecasting, and natural language understanding.Build and fine-tune deep learning models, including RNNs, GRUs, LSTMs , and Transformer architectures (e.g., BERT, T5, GPT).Develop and deploy Generative AI solutions , including RAG pipelines for use cases such as document search, Q&A, and summarization.Perform model optimization techniques such as quantization for improving latency and reducing memory / compute overhead in production.Optionally fine-tune LLMs using Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA .Define and track relevant evaluation metrics; continuously monitor model drift and retrain models as needed.Collaborate with cross-functional teams (data engineering, backend, DevOps) to productionize models using CI / CD pipelines.Write clean, reproducible code and maintain proper versioning and documentation of experiments.RequirementsRequired Skills4–5 years of hands-on experience in machine learning or data science roles.Proficient in Python and ML / DL libraries : scikit-learn, pandas, PyTorch, TensorFlow.Strong knowledge of traditional ML and deep learning, especially for sequence and NLP tasks.Experience with Transformer models and open-source LLMs (e.g., Hugging Face Transformers).Familiarity with Generative AI tools and RAG frameworks (e.g., LangChain, LlamaIndex).Experience in model quantization (e.g., dynamic / static quantization, INT8) and deployment on constrained environments.Knowledge of vector stores (e.g., FAISS, Pinecone, Azure AI Search), embeddings, and retrieval techniques.Proficiency in evaluating models using statistical and business metrics.Experience with model deployment , monitoring , and performance tuning in production environments.Familiarity with Docker, MLflow, and CI / CD practices.