Job Title

We are looking for a skilled and hands-onData Scientistwith 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 optimizingTransformer-based models . Familiarity withquantization ,fine-tuning , andRAG (Retrieval-Augmented Generation)is highly desirable.Exp-3-8 Years Mode-Remote Np-Immediate-15 DaysResponsibilities Design, train, and evaluate ML models for tasks such as classification, anomaly detection, forecasting, and natural language understanding. Build and fine-tune deep learning models, includingRNNs, GRUs, LSTMs , andTransformer architectures(e.g., BERT, T5, GPT). Develop and deployGenerative AI solutions , includingRAG pipelinesfor use cases such as document search, Q&A, and summarization. Performmodel optimization techniquessuch asquantizationfor improving latency and reducing memory/compute overhead in production. Optionally fine-tune LLMs usingSupervised Fine-Tuning (SFT)andParameter-Efficient Fine-Tuning (PEFT)methods likeLoRAorQLoRA . 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.Requirements Required Skills 4–5 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 withTransformer modelsand open-source LLMs (e.g., Hugging Face Transformers). Familiarity withGenerative AItools andRAG frameworks(e.g., LangChain, LlamaIndex). Experience inmodel 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 withmodel deployment ,monitoring , and performance tuning in production environments. Familiarity with Docker, MLflow, and CI/CD practices.