Detalhes do emprego
Roles and responsibilities
- Lead the development of AI products leveraging RAG, LLM, or NLP.|Create scalable, real-world AI solutions that drive business value.
- Collaborate with cross-functional teams, including data scientists, product managers, and software engineers, to create and scale AI solutions.
- Manage the end-to-end lifecycle of AI products, from initial ideation and prototyping to production deployment and post-release improvements.
- Develop models and algorithms for a variety of applications, including text generation, semantic search, information retrieval, and more.
- Optimise models for performance, scalability, and robustness in real-world environments.
- Ensure that AI solutions are aligned with business goals, delivering value and enhancing customer experiences.
- Stay up-to-date with the latest advancements in AI, machine learning, and NLP to continuously improve the company's AI capabilities.
- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
- Proven experience with RAG, LLMs, and/or NLP technologies.
- Hands-on experience in building and deploying AI models from the ground up, taking projects from concept to production.
- Strong programming skills in Python, with experience using AI/ML libraries such as TensorFlow, PyTorch, Hugging Face, or similar frameworks.
- Experience with cloud platforms (preferably Azure) for AI model development and deployment.
Desired candidate profile
- Model Development : Designing and building AI models using machine learning algorithms, deep learning techniques, and natural language processing.
- Data Preparation : Collecting, cleaning, and preprocessing large datasets to ensure data quality and suitability for training AI models.
- Algorithm Implementation : Implementing and optimizing algorithms for specific applications, such as image recognition, recommendation systems, or predictive analytics.
- Performance Evaluation : Evaluating and fine-tuning AI models for accuracy, efficiency, and scalability, using metrics such as precision, recall, and F1 score.
- Collaboration : Working with cross-functional teams, including data scientists, software engineers, and business stakeholders, to integrate AI solutions into existing systems.
- Research and Innovation : Staying updated on the latest advancements in AI and machine learning technologies, and applying new techniques to improve models.
- Deployment : Managing the deployment of AI models into production environments, ensuring they operate effectively and efficiently.
- Monitoring and Maintenance : Continuously monitoring AI systems for performance issues and updating models as necessary to adapt to changing data or business needs.
- Documentation : Creating and maintaining documentation for AI processes, models, and code to ensure clarity and reproducibility.
- Ethics and Compliance : Ensuring that AI solutions comply with ethical standards and regulatory requirements, particularly regarding data privacy and bias.
- Programming Proficiency : Strong coding skills in languages such as Python, R, or Java, along with experience in AI frameworks (e.g., TensorFlow, PyTorch, Keras).
- Mathematics and Statistics : Solid understanding of mathematical concepts, statistics, and algorithms used in machine learning and data analysis.
- Machine Learning Expertise : Deep knowledge of machine learning techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning.
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Hiring company
Michael Page
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