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Your role : design, develop, refining and optimize AI-generated text prompts to ensure they are accurate, engaging, and relevant for various applications. IT includes natural language processing (NLP) models and prompts that drive the performance and effectiveness of language models and conversational AI systems. Generative AI Engineer, you’ll work with generative models and doing prompt engineering to create new & innovative AI products. Collaboration will BE crucial as you focus on creating high-quality prompts, refining model outputs, and enhancing the overall user experience. Your expertise in NLP algorithms, model engineering & prompt engineering techniques will play a vital role in shaping the capabilities and performance of AI language models.
Prompt Engineering – Design and develop high-quality prompts and templates that guide the behavior and responses of language models. Craft prompts to elicit specific information or control the model’s output, ensuring desired accuracy, relevance, and language fluency.
NLP Model Development – Design and develop NLP models, algorithms, and architectures to solve complex language understanding and generation problems. Apply state-of-the-art NLP techniques, including but not limited to text classification, named entity recognition, sentiment analysis, language modeling, and dialogue systems.
Data Analysis & Preprocessing – Analyze and preprocess textual data to prepare IT for NLP model training and evaluation. Apply text cleaning, tokenization, normalization, and other techniques to ensure data quality and consistency.
Model Training & Evaluation : Train and fine-tune NLP models using appropriate algorithms and frameworks. Evaluate model performance using relevant metrics & datasets. Conduct experiments and analysis to improve model accuracy, efficiency, and generalization. Employ techniques like transfer learning and pretraining to leverage existing language models.
Optimize NLP models for speed, memory usage, and resource efficiency, enabling real-time or near-real-time responses. Explore techniques like quantization/model compression/model distillation to reduce model size and inference latency.
Collaboration & Teamwork with data scientists/machine learning engineers/Software engineers, and domain experts to understand business requirements and objectives. Work together to design/develop NLP solutions that address specific needs and enhance the user experience.
Research & Innovation : Stay updated with the latest research advancements and trends in NLP. Explore & experiment with novel techniques, models, and approaches to solve challenging NLP problems.
Document NLP model development processes, methodologies, and results. Clearly communicate complex NLP concepts, findings, and insights to technical and non-technical stakeholders. Present findings, recommendations and progress reports to project teams and management.
To apply for this job please visit www.adzuna.fr.