Senior Data Scientist Position

2 days ago


kanpur, India beBeeMachine Full time

Job Opportunity: Senior Machine Learning EngineerWe are seeking an accomplished data scientist with extensive experience in designing, developing, and deploying machine learning models. This role requires a deep understanding of traditional ML algorithms and advanced deep learning techniques.Create, train, and evaluate ML models for various tasks, including classification, anomaly detection, forecasting, and natural language processing.Build and fine-tune deep learning models using RNNs, GRUs, LSTMs, and transformer architectures such as BERT, T5, GPT.Develop and deploy generative AI solutions, including RAG pipelines for applications like document search, Q&A, and summarization.Implement model optimization techniques to improve latency and reduce memory/compute overhead in production environments.Apply supervised fine-tuning (SFT) and parameter-efficient fine-tuning (PEFT) methods like LoRA or QLoRA to fine-tune large language models.Establish and track relevant evaluation metrics, monitoring model drift and retraining models as necessary.Collaborate with cross-functional teams to integrate models into production workflows using CI/CD pipelines.RequirementsEssential Skills:A minimum of 4-5 years of hands-on experience in machine learning or data science roles.Proficiency in Python and popular ML/DL libraries, including scikit-learn, pandas, PyTorch, and TensorFlow.Strong foundation in traditional ML and deep learning, particularly for sequence and NLP tasks.Familiarity with transformer models and open-source large language models (e.g., Hugging Face Transformers).Experience with generative AI tools and RAG frameworks (e.g., LangChain, LlamaIndex).Knowledge of vector stores (e.g., FAISS, Pinecone, Azure AI Search), embeddings, and retrieval techniques.Ability to evaluate models using statistical and business metrics.Experience with model deployment, monitoring, and performance tuning in production environments.Familiarity with Docker, MLflow, and CI/CD best practices.


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