Role Overview
Seeking 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 Responsibilities
- Develop, 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 & Qualifications
- Strong 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 Skills
- Experience 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).
