As a Prompt Engineer, you will play a crucial role in developing and enhancing the natural language generation capabilities of our AI-powered systems…
You will collaborate with a team of data scientists, machine learning engineers, and software developers to design, implement, and optimize the prompt architecture for our AI models.
The ideal candidate will have a strong background in natural language processing, machine learning, and software engineering.
KEY RESPNSIBILITIES AND ACCOUNTABILITIES:
- Collaborate with data scientists and machine learning engineers to develop prompt architectures for AI models and design effective strategies for natural language generation.
- Implement and optimize prompt engineering techniques to improve the performance and accuracy of language models.
- Collect and preprocess training data, ensuring data quality and integrity for prompt engineering tasks.
- Fine-tune and adapt pre-trained language models to specific domains or applications, leveraging transfer learning techniques.
- Collaborate with software engineers to integrate prompt engineering solutions into existing AI systems and applications.
- Evaluate and benchmark various prompt engineering techniques and propose improvements to enhance model performance.
- Stay updated with the latest research in prompt engineering, natural language processing, and machine learning, and apply relevant advancements to improve the prompt engineering capabilities of our systems.
- Document and communicate prompt engineering strategies, methodologies, and best practices to the wider team.
- Collaborate with data engineers to ensure the availability and integrity of data sources for prompt engineering tasks.
- Contribute to the overall AI system development lifecycle, including testing, debugging, and troubleshooting prompt engineering components.
- Keep track of industry trends and emerging technologies in prompt engineering and contribute to the continuous improvement of our AI systems.
- Minimum 2-3 years' experience in prompt engineering, natural language processing, or a related field, with a strong understanding of machine learning and deep learning techniques.
- Strong proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, or Keras.
- In-depth knowledge of natural language processing techniques, including text classification, named entity recognition, sentiment analysis, and language generation.
- Familiarity with language models such as GPT-3, BERT, or XLNet, and experience fine-tuning and adapting these models for specific applications.
- Solid understanding of machine learning methodologies, including data preprocessing, feature engineering, and model evaluation.
- Experience with text data preprocessing techniques, such as tokenization, stemming, and lemmatization.
- Proficient in building and optimizing data pipelines for large-scale text data processing using tools like Apache Spark or Elasticsearch.
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud, and experience with deploying and managing machine learning models in a cloud environment.
- Familiarity with version control systems like Git, and experience with collaborative software development practices in a team environment.
- Strong problem-solving skills and the ability to analyze and understand complex system requirements.
- Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and effectively communicate technical concepts to non-technical stakeholders.
- Bachelor's degree with a minimum of 4 years' experience, or master's degree with 2 years' experience
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