Gen AI Developer

Gen AI Developer Specialist (6 to 9 years) -

Deep Learning Frameworks: Familiarity with frameworks like TensorFlow, PyTorch, or Keras.
Custom Model Training: Ability to fine-tune and train models on specific datasets, understand overfitting, and apply regularization techniques.
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.
Version Control and Collaboration Tools: Proficiency with tools like Git, which are essential for collaborative development.

Gen AI Lead Developer (10 to 12 years)

Advanced Model Architectures: Deep understanding of and ability to implement advanced neural network architectures like transformers, attention mechanisms, etc.
Scalability: Skills in deploying AI models at scale using cloud platforms and ensuring consistent performance across large user bases.
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.
Multimodal AI: Experience with models that handle various data types (e.g., text, images, sound) simultaneously.
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 performan

Digital & GenAI Analyst (12+ years)

GenAI & Data Science; Digital Architecture & Enablement; Emerging Tech & Innovation Evangelist
Principles and workings of generative models.
Knowledge of saving and loading AI models, such as using formats like ONNX or native formats of deep learning frameworks.
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.