Description
Key Responsibilities:
AI Strategy & Leadership:
- Provide technical guidance and thought leadership on generative AI technologies and their applications.
- Mentor and support junior engineers, promoting skill development and adherence to best practices.
- Collaborate with product leaders and stakeholders to shape AI-driven solutions aligned with business objectives.
Model Development and Fine-tuning:
- Develop and fine-tune state-of-the-art generative AI models, such as large language models and image generation models.
- Implement techniques for model optimization, including quantization, pruning, and knowledge distillation.
RAG Architecture:
- Design and implement robust RAG architectures to enable efficient information retrieval and generation.
- Integrate diverse data sources and knowledge bases into RAG systems.
- Optimize query processing and response generation for optimal performance.
AI System Development:
- Build scalable and reliable AI systems, including APIs, microservices, and data pipelines.
- Collaborate with ML Ops engineers to deploy and manage AI models in production environments.
Experimentation and Innovation:
- Stay up-to-date with the latest advancements in AI research and explore new techniques.
- Conduct experiments to improve model performance and system efficiency.
- Contribute to a culture of innovation and continuous learning.
Qualifications and Experience:
- Bachelor's degree in Computer Science, Computer Engineering, or a related field.
- Overall 7+ years of experience in AI/ML, with a focus on generative AI and RAG architectures.
- Strong programming skills in Python and experience with deep learning frameworks (TensorFlow, PyTorch, etc.).
- Strong and demonstrable skills in GenAI technologies, like OpenAI, Anthropic, or Llama
- Hands-on experience with natural language processing (NLP), computer vision, and machine learning techniques.
- Knowledge of cloud platforms (AWS, Azure, GCP) and their AI/ML services.
- Excellent communication and problem-solving skills