DESCRIPTION

As a Lead AI Engineer, you will lead the development of advanced AI solutions across Aibidia. As a Lead AI Engineer you will design and develop AI models and systems, while ensuring they meet business needs and technical requirements.

SKILLS, KNOWLEDGE AND EXPERTISE

• You have a strong foundation in machine learning, deep learning, Python, ML frameworks (TensorFlow, PyTorch), and language models (LLMs).
• You possess advanced proficiency in Python and experience with other programming languages (e.g., Java, C++) for model development and deployment.
• You have experience in deploying and managing models in production, using MLOps best practices, containerization (e.g., Docker), and orchestration tools.
• You have a strong understanding of data processing, ETL, and data pipeline management
• You have hands-on experience with major cloud platforms (Azure preferably) for AI model training, deployment, and management.
• You have great communications skills in English and ability to work in a cross-functional team.

To absolutely rock the position, these are some of the nice-to-haves:

• Skills in advanced statistical methods and experimental design to refine models and validate results
• Knowledge of MLOps platforms and tools like MLflow, Kubeflow, or TFX for streamlined model management and monitoring
• Understanding of data privacy laws (GDPR, CCPA) and security prac-tices specific to AI, especially in handling sensitive data in model training
• Project management skills in cross team environments

• Lead the design and development of AI models and systems, ensuring they meet business needs and technical requirements
• Manage AI projects from ideation to deployment, coordinating with cross-functional teams
• Optimize model performance for accuracy, and scalability, ensuring the models run efficiently in production
• Work closely with data engineers to ensure AI models can consume and output data seamlessly in real-time and batch environments
• Implement rigorous testing, monitoring, and debugging processes for models in production to ensure reliability and prevent drift
• Stay current with AI trends and emerging technologies, applying the latest research to enhance model accuracy and discover new AI-driven opportunities
• Collaborate with stakeholders, translating complex technical information into business value to align AI initiatives with company goals