Revolutionize private sector use of artificial intelligence and machine learning technologies.As the Applied AI/ML Lead within our dynamic team of innovators and technologists, you will revolutionize how the Private Bank services and advises clients, deepen client engagements, and promote process transformation. You will lead efforts to analyze existing processes and vast amounts of data to design autonomous AI agents, leveraging advanced data analysis, statistical modeling, and AI/ML techniques to solve complex business challenges through high-quality, cloud-centric software delivery. Job Responsibilities: Lead the development and implementation of GenAI and Agentic AI solutions using Python to enhance automation and decision-making processes.Oversee the design, deployment, and management of prompt-based models on LLMs for various NLP tasks in the financial services domain.Conduct and guide research on prompt engineering techniques to improve the performance of prompt-based models within the financial services field, exploring and utilizing LLM orchestration and agentic AI libraries.Collaborate with cross-functional teams to identify requirements and develop solutions to meet business needs within the organization.Communicate effectively with both technical and non-technical stakeholders, including senior leadership.Build and maintain data pipelines and data processing workflows for prompt engineering on LLMs utilizing cloud services for scalability and efficiency.Develop and maintain tools and frameworks for prompt-based model training, evaluation, and optimization.Analyze and interpret data to evaluate model performance and identify areas of improvement. Required qualifications, capabilities, and skills: Formal training or certification on software engineering concepts and  applied experience.Hands-on experience in building Agentic AI solutions.Familiarity with LLM orchestration and agentic AI libraries.Strong programming skills in Python with experience in PyTorch or TensorFlow.Experience building data pipelines for both structured and unstructured data processing.Experience in developing APIs and integrating NLP or LLM models into software applications.Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing.Excellent problem-solving skills and the ability to communicate ideas and results to stakeholders and leadership in a clear and concise manner.Basic knowledge of deployment processes, including experience with GIT and version control systems.Hands-on experience with MLOps tools and practices, ensuring seamless integration of machine learning models into production environments. Preferred Qualifications, Capabilities, and Skills:Familiarity with model fine-tuning techniques.Knowledge of financial products and services, including banking, investment, and risk management.