About the Role
We are looking for an Applied AI Engineer, with a background in building and maintaining production ML systems, to join our team. You will have a unique opportunity to work on cutting-edge AI projects, collaborate with experienced AI/ML engineers, and make a real impact on the world.
Zendy.io, a product of Knowledge E, is a massive online library created to provide individuals with affordable access to a wealth of information in the form of journals, e-books, magazines and other content mediums.
The ideal candidate will have a deep understanding of machine learning and natural language processing, as well as experience with developing and maintaining production-grade AI systems. Reporting directly to our Chief Technology Officer, some of your responsibilities will include:
Responsibilities
- Building and maintaining robust production ML systems
- Ensuring systems are reliable, scalable, and capable of handling real-time data processing and inference
- Utilising AWS SageMaker for model deployment, and monitoring while leveraging AWS services to create scalable and cost-effective ML solutions
- Applying solid software engineering principles in the development and maintenance of ML systems and writing clean, maintainable, and efficient code
- Developing and deploying models using ML frameworks such as Huggingface, PyTorch, TensorFlow, and scikit-learn, ensuring models are well-integrated into the production environment
- Working closely with data scientists, software engineers, and other stakeholders to implement and optimise ML models
Requirements
Minimum Job Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field (a master's degree or equivalent experience is preferred)
- A minimum of 3-5 years of professional experience in applied AI engineering, machine learning, or a related field
- Strong background in building and maintaining production ML systems
- Proficiency in Python and familiarity with ML frameworks: Huggingface, PyTorch, TensorFlow, and scikit-learn
- Solid understanding of AWS SageMaker and other AWS services related to machine learning
- Strong foundation in software engineering, including experience with coding, testing, and version control
- Experience in optimising latency and throughput of machine learning systems and GPU workloads
- Excellent problem-solving skills and attention to detail
- Ability to work in a collaborative, fast-paced environment
- Strong written and verbal communication skills
Desired Extras
- Knowledge of retrieval-augmented generation (RAG) techniques
- Rust programming knowledge