Data Science Lead

As a highly skilled Data Science Lead with hands-on experience in Generative AI, Retrieval-Augmented Generation (RAG), and machine learning model development, your role will involve designing scalable AI systems, optimizing chunking and retrieval strategies, managing vector databases, and fine-tuning large language models (LLMs) for enterprise applications. Key Responsibilities: - Lead the design and deployment of RAG-based GenAI systems using LLMs and embeddings. - Develop and optimize chunking strategies for improved retrieval and context relevance. - Fine-tune transformer-based models (e.g., GPT, LLaMA, Mistral) using LoRA, PEFT, or full-model training. - Implement and manage vector databases (e.g., FAISS, Pinecone, Weaviate) for semantic search. - Build and evaluate machine learning models for classification, regression, clustering, and recommendation systems. - Collaborate with engineering teams to integrate ML and GenAI pipelines into production. - Define and monitor performance metrics for model accuracy, latency, and scalability. - Stay updated with the latest research in GenAI, LLMs, and MLOps. - Mentor junior data scientists and contribute to technical leadership. Required Skills & Tools: - Programming: Python, SQL. - ML Libraries: scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch. - GenAI Frameworks: Hugging Face Transformers, LangChain, OpenAI API. - Vector Databases: FAISS, Pinecone, Weaviate, Qdrant. - LLM Fine-Tuning: LoRA, PEFT, RLHF, prompt tuning. - Data Engineering: Pandas, NumPy, Spark. - Deployment: FastAPI, Docker, Kubernetes, CI/CD. - Cloud Platforms: AWS, GCP, Azure. - Strong problem-solving and communication skills. Qualifications: - Bachelors or Masters degree in Computer Science, Data Science, AI, or related field. - Years of experience in data science, NLP, or machine learning. - Proven track record of building and deploying ML and GenAI applications. Preferred Attributes: - Experience with open-source LLMs (e.g., Mistral, LLaMA, Falcon). - Familiarity with semantic search, prompt engineering, and retrieval optimization. - Contributions to AI research or open-source projects. - Certification in Generative AI, NLP, or ML (e.g., DeepLearning.AI, Hugging Face). As a highly skilled Data Science Lead with hands-on experience in Generative AI, Retrieval-Augmented Generation (RAG), and machine learning model development, your role will involve designing scalable AI systems, optimizing chunking and retrieval strategies, managing vector databases, and fine-tuning large language models (LLMs) for enterprise applications. Key Responsibilities: - Lead the design and deployment of RAG-based GenAI systems using LLMs and embeddings. - Develop and optimize chunking strategies for improved retrieval and context relevance. - Fine-tune transformer-based models (e.g., GPT, LLaMA, Mistral) using LoRA, PEFT, or full-model training. - Implement and manage vector databases (e.g., FAISS, Pinecone, Weaviate) for semantic search. - Build and evaluate machine learning models for classification, regression, clustering, and recommendation systems. - Collaborate with engineering teams to integrate ML and GenAI pipelines into production. - Define and monitor performance metrics for model accuracy, latency, and scalability. - Stay updated with the latest research in GenAI, LLMs, and MLOps. - Mentor junior data scientists and contribute to technical leadership. Required Skills & Tools: - Programming: Python, SQL. - ML Libraries: scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch. - GenAI Frameworks: Hugging Face Transformers, LangChain, OpenAI API. - Vector Databases: FAISS, Pinecone, Weaviate, Qdrant. - LLM Fine-Tuning: LoRA, PEFT, RLHF, prompt tuning. - Data Engineering: Pandas, NumPy, Spark. - Deployment: FastAPI, Docker, Kubernetes, CI/CD. - Cloud Platforms: AWS, GCP, Azure. - Strong problem-solving and communication skills. Qualifications: - Bachelors or Masters degree in Computer Science, Data Science, AI, or related field. - Years of experience in data science, NLP, or machine learning. - Proven track record of building and deploying ML and GenAI applications. Preferred Attributes: - Experience with open-source LLMs (e.g., Mistral, LLaMA, Falcon). - Familiarity with semantic search, prompt engineering, and retrieval optimization. - Contributions to AI research or open-source projects. - Certification in Generative AI, NLP, or ML (e.g., DeepLearning.AI, Hugging Face).