Lead Product Manager, Growth (Remote)

Location : RemoteMandatory Skills : AWS Sagemaker, AWS BedRock, Generative AIJob Overview : AWS Experience - AWS Sagemaker is required, AWS BedRock would be a nice to have.Model Building, Accuracy Metrics, Finetuning - standard Data Science skillset.Proven expertise in model finetuning for LLMs - PEFT, LORA techniques would be a big plus.Able to understand what technique to use for data type.RAG Experience would be great to have - similar to AI Engineer.Machine Learning Engineering : Develop, train, and deploy ML models, ensuring they are optimized for production environments.Create and maintain automated feedback loops to enhance model accuracy and performance.Implement ML pipelines for continuous evaluation and refinement of models in production.AI Orchestration & Integration : Integrate Large Language Models (LLMs) into business applications.Build AI orchestration systems to manage the end-to-end lifecycle of AI models, including deployment and scaling.Work with Vector Databases (VectorDB) to store and query high-dimensional data for AI applications.Set up evaluation metrics and processes to assess model performance over time.Create feedback loops using real-world data to improve model reliability and accuracy.Text-to-SQL & Generative AI-driven Solutions : Develop GenAI-driven Text-to-SQL solutions to automate database queries based on natural language input.Optimize GenAI workflows for database interactions and information retrieval.Embedding / Chunking & Prompt Engineering : Design and implement embedding and chunking strategies for scalable data processing.Utilize prompt engineering techniques to fine-tune the performance of AI models in production environments.Required Qualifications : Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.Proven experience in building, deploying, and maintaining ML models in production environments.Proficiency in programming languages like Python, and frameworks such as TensorFlow, PyTorch, or similar.Familiarity with LLMs, VectorDB, embedding / chunking strategies, and AI orchestration tools.Strong understanding of model evaluation techniques and feedback loop systems.Hands-on experience with Text-to-SQL and prompt engineering methodologies.Knowledge of cloud platforms (AWS) and containerization tools (Docker, Kubernetes).#J-18808-Ljbffr