Large Language Model Engineer - Intelligent Investment Research Direction
Large Language Model Engineer - Intelligent Investment Research Direction
Job Overview Drive the development and deployment of specialized LLMs for investment research automation and augmentation. You'll build custom language models that analyze earnings calls, research reports, and market commentary to generate actionable investment insights, directly supporting portfolio managers and research analysts.
Key Responsibilities
•Fine-tune and deploy LLMs specifically for financial text analysis, sentiment analysis, and investment research
•Develop domain-specific training datasets from earnings calls, analyst reports, regulatory filings, and market commentary
•Implement advanced techniques: RLHF (Reinforcement Learning from Human Feedback), DPO (Direct Preference Optimization), and parameter-efficient fine-tuning
•Build production pipelines for real-time processing of financial news, reports, and alternative text data
•Design evaluation frameworks measuring model performance on financial tasks (sentiment accuracy, entity extraction, summarization quality)
•Collaborate with investment teams to integrate LLM outputs into research workflows and decision-making processes
•Optimize model performance for latency-sensitive applications and cost-effective inference
Required Qualifications
•Master's degree in Computer Science, AI, NLP, or related technical field
•Experience working with large language models, transformers, and NLP systems
•Hands-on experience fine-tuning LLMs using modern techniques
•Strong programming skills in Python with deep learning frameworks (PyTorch, TensorFlow, Hugging Face)
•Experience with distributed training, model optimization, and efficient inference techniques
•Knowledge of financial markets, investment research processes, or asset management workflows
•Understanding of model evaluation, bias detection, and responsible AI practices
Preferred Qualifications
•PhD in NLP, Machine Learning, or related field with focus on language models
•Previous experience in financial services or fintech with LLM applications
•Expertise in multi-modal models combining text, numerical data, and time series
•Knowledge of financial NLP challenges: entity recognition, relationship extraction, sentiment analysis