Seeking an experienced AI/ML Engineer specializing in NLP and LLMs to develop innovative AI solutions, with focus on document processing, conversational AI, and RAG systems. The role combines modern AI/ML engineering practices with practical business applications.
Key Responsibilities:
- Design and develop applications using NLP and LLM technologies for various business needs
- Create intelligent document processing systems using NLP techniques
- Implement conversational AI solutions and chatbots
- Implement and optimize prompt engineering techniques for accurate query generation
- Create language understanding and semantic search applications
- Monitor and evaluate model performance and accuracy
- Design and implement RAG architectures for various business applications
- Fine-tune language models based on specific application requirements, adapting them to the company’s domain, data, and user needs
- Work closely with data scientists, product managers, and software engineers to understand application requirements, translate them into actionable development tasks, and deliver robust NLP-based applications
- Stay up to date on the latest in NLP and LLM advancements, conducting experiments to evaluate the feasibility and potential impact of new techniques on applications
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field
- Strong foundation in machine learning, deep learning, and NLP concepts
- Experience with NLP frameworks and tools (NLTK, spaCy, Hugging Face Transformers)
- Experience with large language models (LLMs) and their applications
- Hands-on experience deploying LLMs in production environments
- Familiarity with data preprocessing, model training, and fine-tuning techniques
- Strong coding skills in Python and proficiency in using RESTful APIs
- Experience working with cloud platforms such as AWS, for deploying and scaling machine learning models
- Familiarity with vector databases, semantic search, or similar NLP-enhanced database technologies
- Knowledge of reinforcement learning for NLP applications
- Experience building production RAG systems
