AI Engineer

Summary

Responsibilities

• Design, train, fine-tune, and assess machine learning and deep learning architectures for diverse applications, including natural language processing, computer vision, predictive analytics, and classification tasks.
  
• Develop and deploy applications leveraging large language models, such as conversational agents, retrieval-augmented generation (RAG) platforms, autonomous AI agents, and streamlined automated processes.
  
• Seamlessly integrate third-party AI APIs and open-source model repositories into existing product frameworks to enhance functionality and performance.
  
• Manage the end-to-end data lifecycle, including the collection, cleansing, labeling, and pipeline construction required for both model training and real-time inference.
  
• Engineer robust deployment pipelines for models using containerization and cloud infrastructure, ensuring high availability and scalability via API services.
  
• Continuously monitor deployed systems for performance metrics, operational costs, and latency, implementing iterative improvements to maintain optimal efficiency.
  
• Collaborate cross-functionally with product management, engineering, and design teams to pinpoint high-impact opportunities where AI can address specific user challenges.
  
• Maintain comprehensive documentation regarding experimental methodologies, architectural decisions, and model performance outcomes.