Design, build and deploy machine learning (ML) and natural language processing (NLP) techniques to text data for classification, summarization and sentiment analysis. Design, develop, and maintain comprehensive data pipelines and frameworks for data preprocessing and streamlined deployment, monitoring, and management of ML applications. Monitor performance and document methodologies and logs to drive improvements and ensure reproducibility. Define the architectural roadmap and technical direction for applied AI and large language model (LLM) initiatives. Lead and mentor a team of AI and ML engineers on GenAI application delivery and best practices across the full development lifecycle. Conduct code and system design reviews, ensuring quality, security, and maintainability. Act as the primary escalation point for complex technical issues related to LLM application deployment and integration. Deploy and architect robust GenAl applications by integrating LLMs into secure, scalable, and reliable systems for orchestrating workflows in high-stakes environments. Direct the design and development of generative Al and Agentic solutions using prompt engineering, RAG, and relevant frameworks. Lead end-to-end development and fine-tuning of custom LLMs and GenAI models, including data preparation and techniques like Low-Rank Adaptation. Integrate, fine- tune, and evaluate custom or off-the-shelf LLMs and other AI models and tools for production deployment. Oversee project timelines, resources, and deliverables for LLM projects. Collaborate with product managers and stakeholders to translate business needs into technical requirements. Drive the long-term AI strategy by identifying new opportunities and communicating complex technical concepts to diverse audiences.

Design, build and deploy machine learning (ML) and natural language processing (NLP) techniques to text data for classification, summarization and sentiment analysis.

Design, develop, and maintain comprehensive data pipelines and frameworks for data preprocessing and streamlined deployment, monitoring, and management of ML applications.

Monitor performance and document methodologies and logs to drive improvements and ensure reproducibility.

Define the architectural roadmap and technical direction for applied AI and large language model (LLM) initiatives.

Lead and mentor a team of AI and ML engineers on GenAI application delivery and best practices across the full development lifecycle.

Conduct code and system design reviews, ensuring quality, security, and maintainability.

Act as the primary escalation point for complex technical issues related to LLM application deployment and integration.

Deploy and architect robust GenAl applications by integrating LLMs into secure, scalable, and reliable systems for orchestrating workflows in high-stakes environments.

Direct the design and development of generative Al and Agentic solutions using prompt engineering, RAG, and relevant frameworks.

Lead end-to-end development and fine-tuning of custom LLMs and GenAI models, including data preparation and techniques like Low-Rank Adaptation.

Integrate, fine- tune, and evaluate custom or off-the-shelf LLMs and other AI models and tools for production deployment.

Oversee project timelines, resources, and deliverables for LLM projects.

Collaborate with product managers and stakeholders to translate business needs into technical requirements.

Drive the long-term AI strategy by identifying new opportunities and communicating complex technical concepts to diverse audiences.