Key Responsibilities:
- Generative AI Development: Design, develop, and deploy innovative AI solutions, with a primary focus on applications utilizing Generative AI models and large language models (LLMs).
- Prompt Engineering Expertise: Apply and refine advanced prompt engineering techniques to optimize the performance, output quality, and utility of Gen AI models for various use cases.
- UI Prototyping and Development: Develop and iterate on simple, interactive user interfaces for AI applications and prototypes using Python frameworks (e.g., Streamlit, Flask, Gradio) and potentially basic JavaScript, ensuring these UIs effectively showcase AI capabilities and can integrate with relevant backend services for data interaction, user management, and rapid deployment.
- AI Integration: Integrate AI capabilities into existing software systems and applications, ensuring seamless functionality and enhanced user experience through robust API development and consumption.
- System Optimization: Continuously optimize AI systems for scalability, performance, and real-time responsiveness.
Qualifications and Skills:
- Bachelor's degree (Master's preferred) in Computer Science, Data Science, or a related field.
- Proven experience in AI development, with a proven track record of successfully delivering Gen AI/ AI projects
- Strong proficiency in Python is mandatory. Demonstrated ability to use Python for backend AI development, scripting, and building simple user interfaces
- Familiarity with JavaScript for front-end interface development is a plus.
- Solid understanding of prompt engineering principles and best practices for interacting with and optimizing large language models (LLMs). Experience with various prompt strategies (e.g., few-shot, chain-of-thought) is highly valued.
- Experience integrating AI solutions into applications, including developing and consuming RESTful APIs. Familiarity with connecting UIs to backend services for features like authentication, data storage, or rapid deployment (e.g., Firebase, particularly within the GCP ecosystem) is a plus.
- Familiarity with foundational Machine Learning concepts, traditional AI models, and related libraries (e.g., TensorFlow, PyTorch) is beneficial.
- Experience with cloud platforms like GCP (e.g., Vertex AI, with specific mention of Gemini capabilities if applicable) and CI/CD pipelines.
