Senior Quant Developer

• Design and Develop LLMs: Build, train, and fine-tune LLMs and generative AI models for real-world applications, focusing on tasks such as document understanding, information extraction, question answering, and multimodal representation learning
• State-of-the-Art Research: Stay at the forefront of AI advancements, particularly in NLP, multimodal learning, and generative AI
• Model Deployment: Collaborate with engineering teams to deploy models into production environments
• MLOps Practices: Contribute to the development of MLOps toolkits to streamline the machine learning lifecycle
• Define Validation Metrics: Develop robust metrics and frameworks to evaluate model accuracy, reliability, and robustness
• Conduct Validation Assessments: Perform rigorous testing, including regression analysis, unit testing, and performance benchmarking
• Explore and Establish LLMs as Judges: Develop and implement methodologies for using LLMs as evaluators to assess the quality and accuracy of other AI models
• Documentation & Reporting: Maintain detailed documentation of model development, validation processes, and performance metrics
• Collaborate with Governance Teams: Work closely with internal compliance and governance teams to ensure models adhere to organizational policies and regulatory requirements

• Bachelor's, Master's, or Doctorate degree in Machine Learning, Natural Language Processing (NLP), Computer Science, Data Science, Statistics, or a related field
• Hands-on Experience with LLMs: Strong expertise in developing, fine-tuning, and deploying LLMs and generative AI models
• Programming Skills: Proficiency in Python, with experience in libraries such as PyTorch, TensorFlow, Hugging Face Transformers, and tools like LangChain or LlamaIndex
• NLP & Multimodal Learning: Deep understanding of NLP techniques, multimodal representation learning, and document intelligence
• Validation & Testing: Familiarity with unit testing frameworks, performance testing tools, and statistical methods for model evaluation
• MLOps Knowledge: Understanding of machine learning lifecycle management, model monitoring, and deployment pipelines
• Proven experience in both developing and validating AI/ML models, particularly in the context of LLMs

Experience with document understanding, generative AI, or multimodal models is a plus

• Continuous professional development
• Flexible working
• Opportunities to grow within an inclusive and diverse environment
• Equal opportunity employer