GenAI - Lead

How You Will Make an Impact:

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 Training

  Oversee 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.
• Conduct A/B testing and user feedback loops to enhance AI performance.

What You Bring to the Team:

• 7+ years of experience in AI/ML engineering, including research, model development, and deployment.
• Strong background in deep learning frameworks such as TensorFlow, PyTorch, or JAX.
• Expertise in optimizing large-scale machine learning models for efficiency and performance.
• Experience in leading AI teams and managing end-to-end AI lifecycle development.
• Familiarity with cloud-based AI solutions (AWS, GCP, Azure) and MLOps best practices.
• Strong understanding of data privacy laws and ethical AI considerations.
• Minimum Bachelors Degree in Computer Science, AI, Machine Learning, or a related field. Master s/Ph.D. preferred.