Generative AI Engineer

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.
• Ensure adherence to security, compliance, and responsible AI practices.
  

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 (Master’s degree preferred).
• 6+ years of experience in AI/ML, with a strong focus on generative AI and RAG architectures.
• Proven experience in leading or contributing to complex AI projects from ideation to deployment.
• Strong programming skills in Python and experience with deep learning frameworks (TensorFlow, PyTorch, etc.).
• Strong and demonstrable skills in GenAI technologies, such as 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.
• Familiarity with MLOps, CI/CD pipelines, and containerization (Docker, Kubernetes).
• Excellent communication, problem-solving, and stakeholder management skills.
• Ability to balance research-oriented exploration with practical engineering execution.