Deep Learning Applications Developer
il y a 6 jours
We are seeking a highly skilled Deep Learning Applications Developer to join our team at RECRUIT2retail AUSTRALIA. As a Deep Learning Applications Developer, you will be responsible for designing, building, and maintaining AI applications utilizing deep learning (DL) models. You will work closely with our data science team to drive experimentation, rapid iteration, and the implementation of scalable AI solutions.
Key Responsibilities- Develop DL-native applications: Design, build, and maintain AI applications utilizing deep learning (DL) models, embracing an experimentation-driven process to unlock new capabilities.
- Deploy and optimize ML/DL models: Ensure scalability, performance, and reliability in production environments, moving from experimental to production-grade solutions.
- Collaborate and iterate: Work closely with data scientists and product teams to integrate models and solutions into business operations, rapidly iterating through experimentation and proof-of-concepts (PoCs).
- Implement MLOps best practices: Ensure the continuous integration and delivery of AI models through MLOps pipelines, incorporating monitoring and feedback loops.
- Productization: Turn experimental models into production-ready applications by applying best practices in software engineering, cloud architecture, and feedback mechanisms.
- 3+ years of experience deploying AI models: Proven experience in putting ML/DL models into production environments at scale.
- Expertise in building AI applications: Hands-on experience building AI-driven applications and services, with a focus on delivering practical solutions from experimental models.
- Experience with Large Language Models (LLMs): Strong understanding of LLMs, including their experimentation and productionization.
- Vector databases: Experience with vector databases for enhanced retrieval and AI performance.
- Proficiency in Python and cloud platforms: Deep expertise in Python and experience with cloud platforms such as AWS, GCP, or Azure.
- Research and experimentation mindset: Ability to design, experiment, and iterate with LLMs and AI models, balancing lean experimentation and structured workflows.
- MLOps knowledge: Familiarity with MLOps pipelines, monitoring, and optimization techniques for continuous delivery and feedback integration.
- Problem-solving and adaptability: Ability to navigate a fast-paced environment, embracing agility and continuous improvement in AI solution development.
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