Job Title

Role OverviewSeeking an experienced Data Scientist with strong hands-on expertise in Generative AI, Retrieval-Augmented Generation (RAG), and Deep Learning. The role involves building, deploying, and optimizing AI/ML models to solve complex business problems, enhance automation, and develop scalable GenAI applications.Key ResponsibilitiesDevelop, fine-tune, and deploy Generative AI models (LLMs, diffusion models, transformers).Design and implement RAG pipelines using vector databases, embeddings, and retrieval frameworks.Build, train, and optimize deep learning models for NLP, computer vision, and multimodal tasks.Create scalable end-to-end ML pipelines for production environments.Conduct data preprocessing, feature engineering, and experimentation.Evaluate model performance with appropriate metrics and optimize for accuracy, latency, and efficiency.Collaborate with engineering, product, and business teams to integrate AI solutions into applications.Research emerging GenAI and DL techniques to enhance existing systems.Document architecture, workflows, and best practices.Required Skills & QualificationsStrong proficiency with Python and ML frameworks (PyTorch, TensorFlow, Keras).Solid understanding of LLMs, embeddings, transformers, and prompt engineering.Experience with RAG frameworks (LangChain, LlamaIndex, Haystack) and vector databases (FAISS, Pinecone, Milvus, Chroma).Hands-on experience with deep learning architectures (CNNs, RNNs, attention models).Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).Strong knowledge of data handling, NLP, and model deployment.Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or equivalent.Preferred SkillsExperience with MLOps tools (MLflow, DVC, Weights & Biases).Exposure to multimodal AI (text, image, audio).Experience working with LLM fine-tuning (LoRA, QLoRA, PEFT).Knowledge of retrieval optimizations (hybrid search, rerankers, BM25).