- Design and develop GenAI models and applications from concept to production, including model selection, fine-tuning, and optimization for specific business use cases.
- Build end-to-end AI solutions that integrate seamlessly with existing enterprise systems and workflows.
- Implement sophisticated prompting strategies and language engineering techniques to maximize model performance and reliability.
- Create functional demonstration interfaces and prototypes using tools like Gradio, Streamlit, or custom web applications.
- Stay current with the latest developments in GenAI, including models from OpenAI, Google, Anthropic, and open-weight alternatives.
- Build robust, scalable APIs and microservices that serve AI models in production environments.
- Develop containerized applications using Docker and orchestration platforms for reliable deployment.
- Create and maintain clean, well-documented code that follows best practices for enterprise software development.
- Implement proper error handling, logging, and monitoring for AI applications.
- Collaborate with cross-functional teams using agile development methodologies.
- Design and implement robust data pipelines for preparation, cleaning, and integration of diverse data sources.
- Handle enterprise data challenges including Excel files, PowerPoint presentations, and Office 365 integrations.
- Build ETL processes that ensure data quality and consistency for AI model training and inference.
- Implement data processing solutions that scale efficiently with growing data volumes.
- Develop data validation and monitoring systems to maintain pipeline reliability.
- Integrate AI solutions with existing business systems, databases, and enterprise applications.
- Navigate complex enterprise environments and work with legacy systems and data formats.
- Implement security best practices and ensure compliance with enterprise governance requirements.
- Manage model lifecycle including version control, A/B testing, and performance monitoring.
- Build solutions that handle enterprise-scale data processing and user loads.
- Experiment with emerging GenAI technologies, frameworks, and methodologies
- Conduct applied research to solve novel business problems using state-of-the-art AI techniques.
- Evaluate and benchmark different AI models and approaches for specific use cases.
- Contribute to the lab's knowledge base and share learnings across the team
- Participate in the broader AI community through conferences, publications, or open-source contributions.
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