AI Application Engineer
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Key details
Job Description
- Design and develop 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.
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