Overview

How You Will Make an Impact:

Responsibilities

• AI Research Development
• Design, develop, and fine-tune state-of-the-art generative AI models (e.g., GPT, Stable Diffusion, DALL E, BERT, LLaMA, T5, etc.).
• Optimize LLMs, multimodal AI, and other generative models for performance, efficiency, and scalability.
• Stay up to date with the latest advancements in the Generative AI domain.
• Implement techniques such as transfer learning, model compression, reinforcement learning (RLHF), and adversarial training.Technical Leadership Team Management
• Lead and mentor a team of AI engineers.
• Define and enforce best practices for AI model development, MLOps, and deployment.
• Collaborate with cross-functional teams, including product, design, data engineering, and software engineering.
• Drive AI project roadmaps and ensure timely execution while balancing research and productization.AI System Architecture Infrastructure
• Design and implement scalable, high-performance AI architectures for real-world applications.
• Work with cloud-based AI services (AWS, GCP, Azure) and on-premise solutions to deploy AI models.
• Ensure AI models are optimized for low latency, cost efficiency, and high throughput.Data Engineering Model TrainingOversee data collection, augmentation, and preprocessing pipelines for training generative AI models.
• Implement self-supervised learning, synthetic data generation, and data labeling strategies.
• Ensure AI models comply with data privacy regulations (GDPR, CCPA, etc.).Performance Monitoring Optimization
• Implement real-time monitoring, logging, and debugging for AI models in production.