Northern Trust, a Fortune 500 company, is a globally recognized, award-winning financial institution that has been in continuous operation since 1889. Northern Trust is proud to provide innovative financial services and guidance to the world's most successful individuals, families, and institutions by remaining true to our enduring principles of service, expertise, and integrity. With more than 130 years of financial experience and over 22,000 partners, we serve the world's most sophisticated clients using leading technology and exceptional service. The Enterprise AI team at Northern Trust performs data science research and develops custom prototypes to problems that 1) are not adequately addressed by available commercial solutions, 2) enhance the customer and/or internal partner experience and 3) provide Northern Trust with a competitive advantage. We are at the forefront of rolling out Generative AI solutions and frameworks for the bank from Proof of Concepts, full stack development of solutions and vendor evaluations. We also serve to inspire and empower applied data science practitioners by facilitating high-quality data science education and collaboration within Northern Trust as well as with top universities and research institutions. As a Senior AI Engineer, you will be a key player in developing, deploying, and maintaining state-of-the-art Generative AI solutions within the Azure cloud environment. Your role will focus on building and refining Large Language Models (LLM) and RAG systems on Azure infrastructure, ensuring they meet business requirements and enterprise-level standards. As a member on the team, a candidate will be expected to: Develop software, typically in Python, to independently acquire data from disparate sources (databases, files, APIs, etc.) and combine them into appropriate training, validation and testing datasets. Analyze raw datasets using descriptive statistics, working directly with domain experts to understand the meaning of data fields Have a deep understanding of Generative AI and LLMs, evaluation metrics and theory behind LLMs & conversational AI, since Gen AI will be a core part of many solutions being developed at the Bank. Be able to explain the full pattern of Retrieval Augmented Generation (RAG) and make recommendations on which specifics technology and techniques to use when building solutions based on this pattern. In most cases the candidate will be developing and integrating Retrieval-Augmented Generation (RAG) systems on Azure cloud. Leverage open-source AI software like TensorFlow, PyTorch, HuggingFace, LangChain, AutoGen, LangGraph and LamaIndex for the solution development and evaluation. Evaluate various AI frameworks and services for efficacy and make recommendations on their inclusion as standardized tooling for AI development. Integrate these tools and software with Azure services for a seamless development and deployment experience. IAC and automation - leverage Terraform for automating cloud provisioning of resources and other infrastructure tasks. Build unit tests, data quality checks and data pipelines to ensure that algorithms use trusted data. Implement CI/CD pipelines using Azure DevOps, ML Ops, Github Actions, TerraForms for the automated deployment of AI solutions. Utilize Azure Kubernetes Service (AKS) for managing and scaling containerized AI applications. Regularly update and maintain the AI models, ensuring their relevance and effectiveness using Azure Monitory tools. Enforce strict security measures and controls in Azure, including network security configurations and identity management, data encryption and privacy. Comply with industry standards, best practices and regulations for AI solutions. Work across multiple projects in a fluid environment where work is required across the full research lifecycle from forming a hypothesis, acquiring data, and developing ETL-style software to presenting findings. Provide guidance to other software development teams as prototypes and frameworks are engineered for full production environments