Knowledge of saving and loading AI models, such as using ONNX or native formats of deep learning frameworks.
Should be strong in Python backend development.
Cloud platforms like AWS, GCP, or Azure, especially services related to AI and ML.
Containerization tools like Docker to package the application and its dependencies.
GPUs, TPUs, or other accelerators, and how to leverage them for AI inference.
Techniques like model quantization, pruning, and distillation to improve inference speed and reduce memory footprint.
Distribute incoming application traffic across multiple instances to ensure optimal resource utilization.
Set up monitoring tools to track the health, uptime, and performance of the deployed application.
Secure deployment of applications, including encryption, authentication, and authorization mechanisms.
Data protection principles, especially when handling user data or other sensitive information.
Proficiency with tools like Git.
CI/CD pipelines and tools like Jenkins, Travis CI, or GitHub Actions.
Networking principles to ensure the application is accessible and communicates effectively with other services or databases.
Integrating databases to store or retrieve data, especially if the AI application requires real-time data access.
Requirements & Skills:
14+ years of experience is required.
A minimum of 3+ years of relevant experience in Gen AI is required.
Advanced Model Architectures: Deep understanding of and ability to implement advanced neural network architectures like transformers, attention mechanisms, etc., and understanding of fine-tuning LLMs.
Development: Python
Cloud and LLM: Experience with Azure services, vector Databases, and LLMs like Azure OpenAI and OpenAI.
Scalability: Skills in deploying AI models at scale using cloud platforms and ensuring consistent performance across large user bases.
Data Engineering: Understanding various tools and techniques for engineering data for GenAI processing, data extraction techniques from different types of documents.
Integration Skills: Proficiency in integrating AI functionalities into applications, web services, or mobile apps.
Optimization: Knowledge of optimizing model performance and reducing latency for real-time applications.
Security and Ethics: Knowledge of potential vulnerabilities in AI (e.g., adversarial attacks), mitigation strategies, and the ethical considerations of AI deployment.
Research Acumen: Ability to read, understand, and implement findings from the latest AI research papers.
Domain-Specific Knowledge: Depending on the application, advanced developers might need deep knowledge in specific areas, such as medical imaging, financial forecasting, etc.
Continuous Integration/Continuous Deployment (CI/CD): Skills in automating the testing and deployment of AI models, ensuring that models are always up-to-date and performing.
Bachelor’s degree in Information Technology, Computer Science, or related field is required.
