Machine Learning Engineer

23 hours ago

Jamnagar, India beBeeData Full time

Senior Data Scientist PositionWe are seeking an experienced Senior Data Scientist with strong hands-on expertise in Generative AI, Retrieval-Augmented Generation (RAG), and Deep Learning.Main Responsibilities:Develop and Deploy AI/ML Models: Build, deploy, and optimize AI/ML models to solve complex business problems.RAG Pipeline Development: Design and implement RAG pipelines using vector databases, embeddings, and retrieval frameworks.Deep Learning Model Training: Build, train, and optimize deep learning models for NLP, computer vision, and multimodal tasks.Scalable ML Pipelines Creation: Create end-to-end ML pipelines for production environments.Data Preprocessing and Engineering: Conduct data preprocessing, feature engineering, and experimentation.Model Evaluation and Optimization: Evaluate model performance with appropriate metrics and optimize for accuracy, latency, and efficiency.Collaboration and Integration: Collaborate with engineering, product, and business teams to integrate AI solutions into applications.Research and Innovation: Research emerging GenAI and DL techniques to enhance existing systems.Knowledge Sharing and Documentation: Document architecture, workflows, and best practices.Required Skills and Qualifications:Python and ML Frameworks Expertise: Strong proficiency with Python and ML frameworks (PyTorch, TensorFlow, Keras).LLMs and Embeddings Understanding: Solid understanding of LLMs, embeddings, transformers, and prompt engineering.RAG Frameworks Experience: Experience with RAG frameworks (LangChain, LlamaIndex, Haystack) and vector databases (FAISS, Pinecone, Milvus, Chroma).Deep Learning Architectures Knowledge: Hands-on experience with deep learning architectures (CNNs, RNNs, attention models).Cloud Platforms Familiarity: Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).Data Handling and NLP Expertise: Strong knowledge of data handling, NLP, and model deployment.Educational Background: Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or equivalent.Preferred Skills:MLOps Tools Experience: Experience with MLOps tools (MLflow, DVC, Weights & Biases).Multimodal AI Exposure: Exposure to multimodal AI (text, image, audio).LLM Fine-Tuning Experience: Experience working with LLM fine-tuning (LoRA, QLoRA, PEFT).Retrieval Optimizations Knowledge: Knowledge of retrieval optimizations (hybrid search, rerankers, BM25).

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