Gen AI Lead Developer

Required Experience:

• 14+ years of experience is required.
• A minimum of 3+ years of relevant experience in Gen AI is required.

Responsibilities:

• Principles and workings of generative models.
• 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.

Essential Skills:

• 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.

Essential Qualification: 

Bachelor’s degree in Information Technology, Computer Science or related field is required.