Objective
The Generative AI Full Stack Engineer will be responsible for designing, developing, and maintaining web applications that incorporate generative AI models. This role requires a strong understanding of both front-end and back-end technologies, as well as experience in deploying and integrating AI models into scalable applications.
Key Responsibilities
- Design and develop end-to-end web applications that utilize generative AI models.
- Implement and maintain front-end interfaces using modern web technologies (HTML, CSS, JavaScript, React, Angular, or Vue.js).
- Develop and manage back-end services and APIs to support AI functionalities (Node.js, Django, Flask, or similar frameworks).
- Integrate generative AI models into web applications, ensuring seamless user interactions and performance.
- Collaborate with AI engineers and data scientists to understand model requirements and deployment needs.
- Implement and manage databases (SQL and NoSQL) to store and retrieve application data efficiently.
- Ensure the security, scalability, and reliability of web applications.
- Develop automated tests to ensure the quality and performance of web applications.
- Monitor application performance, troubleshoot issues, and optimize components for maximum efficiency.
- Stay updated with the latest advancements in web development and AI technologies.
Required Skills and Qualifications:
- Bachelor's degree in Computer Science, Engineering, or a related field.
- Proven experience as a Full Stack Engineer with a focus on AI/ML applications.
- Proficiency in front-end technologies (HTML, CSS, JavaScript, React, Angular, or Vue.js).
- Strong experience with back-end development (Node.js, Django, Flask, or similar frameworks).
- Knowledge of AI/ML concepts and experience with AI/ML libraries (e.g., TensorFlow, PyTorch).
- Experience with database management (SQL and NoSQL).
- Familiarity with cloud platforms (AWS, Azure, Google Cloud) and their AI services.
- Understanding of RESTful APIs and GraphQL.
- Experience with version control systems (e.g., Git).
- Excellent problem-solving, analytical, and communication skills.
- Ability to work collaboratively in a fast-paced, team-oriented environment.Experience with deploying AI models in production environments.
- Knowledge of DevOps practices and CI/CD pipelines.
- Familiarity with containerization and orchestration tools (Docker, Kubernetes).
- Understanding of microservices architecture.
- Experience with performance optimization and security best practices.
