Job Description
Actively participate in designing, developing, and optimizing the Large Language Model (LLM) to work efficiently for financial use cases, with a hands-on approach to fine-tuning the model for domain-specific terminology and context. This includes tasks such as data preprocessing, model training, and performance evaluation to ensure the LLM meets the specific needs of financial applications. Additionally, leverage prompt engineering techniques to guide the LLM to produce desired responses, which involves crafting effective prompts, iteratively refining them based on the model's performance, and experimenting with different prompt structures to enhance accuracy and relevance. Stay Up-to-date with the latest trends and advancements in AI, machine learning, and prompt engineering, particularly in relation to the financial industry, to ensure the product remains competitive and cutting-edge. This requires continuous research, attending conferences, and engaging with industry publications to identify emerging technologies and best practices. Collaborate closely with product managers and other relevant teams in a hands-on capacity to understand product requirements and customer needs, incorporating this feedback directly into model development and prompt engineering. This includes regular communication sessions, collaborative workshops, and iterative feedback loops to align the model's capabilities with business objectives.
Key Responsibilities
- Lead the end-to-end development and optimization of LLMs tailored for financial applications, ensuring they deliver high accuracy and efficiency in tasks like risk assessment, fraud detection, and financial reporting.
- Design and implement advanced prompt engineering strategies to improve the model's output quality, including creating context-aware prompts and refining them through A/B testing and performance analysis.
- Monitor and analyze industry developments in AI and machine learning, particularly in financial technology, to identify opportunities for model enhancement and innovation.
- Collaborate with cross-functional teams to translate business requirements into technical specifications, ensuring the model's functionality aligns with user needs and organizational goals.
- Document and share insights from model training, optimization, and prompt engineering processes to support team knowledge transfer and future project development.
Job Requirements
- Proven experience in LLM development, with a strong background in financial domain knowledge and natural language processing (NLP) techniques.
- Advanced understanding of prompt engineering methodologies, including prompt design, iteration, and evaluation frameworks.
- Ability to stay current with AI and machine learning trends, particularly in financial technology, through continuous learning and professional engagement.
- Excellent collaboration and communication skills to work effectively with product managers, data scientists, and stakeholders in a dynamic team environment.
- Strong analytical skills to interpret model performance metrics and customer feedback, driving data-driven decisions for improvement.
- Proficiency in programming languages such as Python, along with experience in machine learning frameworks like TensorFlow or PyTorch.
- Knowledge of financial regulations and compliance requirements to ensure model outputs meet industry standards and legal obligations.
- Ability to manage multiple projects simultaneously, prioritizing tasks to meet deadlines while maintaining high-quality deliverables.
