Develop, implement, and maintain robust machine learning models using GenAI agentic framework.
Engage in prompt engineering to optimize the interaction with large language models, with other AI systems or tools.
Utilize techniques like Retrieval-Augmented Generation (RAG) to enhance AI solutions with real-time information retrieval.
Develop and design graph databases using tools such as NetworkX or AWS Neptune for relationship-oriented data modeling.
Fine-tune Large Language Models (LLMs) to tailor solutions to specific business needs and improve model efficiency.
Leverage AWS services (including S3, ECS, ECR, Lambda, SageMaker, and more) to build scalable machine learning solutions.
Utilize data engineering skills with Glue and PySpark for efficient data preparation and processing.
Conduct model evaluation and testing to ensure accuracy, reliability, and robustness.
Monitor machine learning models in production, implementing strategies for ongoing performance tracking and optimization.
Ensure the security of models and data through secure API integration, utilizing tokens and comprehensive data security principles.
Govern models in compliance with the Machine Learning Development Lifecycle (MDLC) and Machine Learning Production Lifecycle (MPLC) processes.
Collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to deliver impactful machine learning solutions.
Stay up-to-date with the latest advancements in machine learning, generative AI technologies, and methodologies.