AI/ML Engineer

Key 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. Qualifications: 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.