AI Developer

Role & responsibilities

LLM & AI Model Development

• Develop, fine-tune, and deploy Large Language Models (LLMs) for various language processing tasks such as entity recognition, text summarization, intent analysis, and fraud detection.
• Design, train, and validate NLP and LLM-based models to ensure they meet the needs of digital identity verification and fraud prevention.
• Continuously monitor the performance of AI models and implement strategies to improve accuracy, reliability, and scalability.
• Retrain models based on real-world data to ensure optimal performance in production environments.

Collaboration & Integration

• Work closely with product managers to define AI-driven features and objectives for digital identity verification and fraud detection.
• Partner with engineers to integrate LLMs and AI models seamlessly into production systems, ensuring scalability and smooth operation.
• Collaborate with data scientists to gather insights and improve model performance.

Data Management & NLP Analytics

• Handle large datasets: clean, extract features, and prepare data inputs for LLMs and machine learning models.
• Use advanced analytics tools to evaluate model performance, identify potential issues, and optimize models for NLP tasks.
• Ensure efficient data preprocessing and management for continuous model improvements.

Compliance, Security & Model Ethics

• Ensure AI models comply with data privacy, security, and regulatory standards specific to the fintech industry.
• Implement robust fraud detection algorithms and conduct regular audits to maintain fairness, security, and privacy across AI models.
• Address ethical considerations in AI model deployment and ensure transparency in how models are used and maintained.

Experimentation & Optimization

• Lead A/B testing and other validation techniques to experiment with and optimize LLM and AI models.
• Document results of model experimentation, performance outcomes, and improvement efforts.
• Ensure ethical practices in AI model development and provide transparency for all stakeholders.

Basic Qualifications:

• Strong proficiency with LLM frameworks (e.g., Hugging Face Transformers, GPT-based models) and machine learning libraries (e.g., TensorFlow, PyTorch).
• Expertise in Natural Language Processing (NLP) and a proven track record of deploying and maintaining LLMs in production environments.
• Knowledge of data privacy, security, and compliance requirements in fintech.
• Solid experience in problem-solving and collaborating within cross-functional teams to deliver AI-driven products.
• Familiarity with fraud detection models and digital identity verification.

Preferred Qualifications:

• Experience in working with large-scale data pipelines and high-performance AI systems.
• Experience in deploying AI and NLP models in cloud environments (e.g., AWS, Google Cloud, Azure).
• Familiarity with AI model performance evaluation techniques and optimizing for speed and accuracy.