AI Engineer

• Lead the end-to-end technical development, deployment, monitoring, and maintenance of real-world, production-level Generative AI solutions within a financial context.
• Apply advanced NLP techniques to financial data, refining prompt engineering strategies for Large Language Models (LLMs) to achieve optimal performance and desired outcomes.
• Collaborate extensively with business stakeholders to translate complex business needs into robust and scalable GenAI technical requirements and solutions.
• Develop, test, and maintain high-quality Python code for GenAI applications, integrating with various data sources, APIs, and vector databases.
• Design and implement scalable API architectures for GenAI applications, ensuring seamless integration and efficient data flow.
• Proactively troubleshoot and debug GenAI models in production environments, quickly identifying and resolving issues to maintain system stability and performance.
• Monitor and optimize MLOps pipelines for GenAI models, ensuring efficient training, deployment, and continuous integration/continuous delivery (CI/CD).
• Stay rigorously up to date with the rapidly evolving Generative AI landscape, continuously researching and evaluating new tools, techniques, LLM architectures, and emerging technologies.
• Participate actively in team meetings, contributing to strategic discussions, technical design reviews, and knowledge sharing sessions.
• Communicate complex technical concepts and GenAI capabilities clearly and effectively to non-technical stakeholders, translating technical jargon into understandable business terms.
• Ensure adherence to best practices in MLOps, model governance, data privacy, and responsible AI principles throughout the development lifecycle.
• Expert-level Python programming skills are mandatory. This includes deep familiarity with core Python, as well as extensive proficiency in key libraries for AI/ML and GenAI applications:

• Data Structures: Lists, dictionaries, sets, etc.
• Scientific Computing: NumPy, Pandas, SciPy.
• Machine Learning: Scikit-learn, XGBoost, LightGBM.
• Deep Learning: TensorFlow, PyTorch.
• Generative AI specific Libraries: Transformers, LangChain

Apptad