01Overview
Design and develop application solutions using generative models, RAG and vector database and vector search.
Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems.
Research and stay current on the latest advancements in generative AI technologies, methodologies, and best practices.
Optimize and fine-tune generative models for performance, scalability, and efficiency.
Troubleshoot and resolve issues related to generative AI models, implementations, and workflows.
Create and maintain comprehensive documentation for generative AI models and their applications.
Build effective data pipelines and manage large datasets for model training and evaluation.
Measure model outputs using appropriate metrics, with awareness of bias and fairness issues.
Implement and use AI coding assistants (e.g., GitHub Copilot) and version control (Git).
Conduct rigorous testing and build scalable, maintainable systems.
Read, analyze, and implement recent AI research papers; conduct experiments as needed.
Communicate complex technical concepts and findings to non-technical stakeholders.
Required Qualifications:
Robust proficiency in Python and effective prompt engineering techniques; familiarity with other programming languages is a plus.
Hands-on experience with leading generative text models (e.g., Claude, OpenAI GPT, Gemini), including model fine-tuning and customization.
Proficiency in AWS Bedrock, including model access and knowledge base implementations; experience with Azure OpenAI.
Solid experience with AWS serverless architecture; familiarity with Azure or GCP is desirable.
Experience building and optimizing data pipelines for handling large-scale datasets.
Knowledge of metrics for evaluating model performance, including bias and fairness considerations.
Experience with AI coding assistants (e.g., GitHub Copilot), version control systems (Git), and scalable system design.
Excellent documentat .
