AI Application Engineer

• Design and develop robust toolchains to support and accelerate multimedia AI activities.
• Develop specialized AI agents and orchestration architectures to work together seamlessly on complex goals.
• Implement, deploy, and scale multimedia Machine Learning (ML) pipelines across Google Cloud and edge devices.
• Optimize machine learning models for improved performance, latency, and deployment efficiency.

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience with software development and engineering.
• Experience coding in one or more general-purpose programming languages (e.g., Python and C++).
• Experience with Machine Learning frameworks (e.g., TensorFlow, PyTorch, or JAX).
• Experience implementing, deploying, and maintaining machine learning models and pipelines in production environments.

Preferred qualifications:

• Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Experience with multimedia applications, video processing, or computer vision ML tasks.
• Experience building and scaling ML pipelines on cloud infrastructure (e.g., Google Cloud Platform) and deploying optimized models to edge devices (e.g., mobile, embedded systems).
• Experience with model optimization techniques for performance and latency reduction (e.g., quantization, pruning, hardware-aware tuning).
• Familiarity with AI agent architectures, large language models (LLMs), or orchestration systems (e.g., LangChain, AutoGen).
• Strong understanding of distributed systems, system architecture, and toolchain development for engineering teams.

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience with software development and engineering.
• Experience coding in one or more general-purpose programming languages (e.g., Python and C++).
• Experience with Machine Learning frameworks (e.g., TensorFlow, PyTorch, or JAX).
• Experience implementing, deploying, and maintaining machine learning models and pipelines in production environments.