- 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 efficient 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 :

- Strong 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 documentation skills and experience collaborating with multi-disciplinary teams.

- Strong communication skills, with the ability to present technical concepts to non-technical audiences.