\\\\Our Purpose\\\\ \Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, were helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.\\ \\\\Title and Summary\\\\ Senior AI Engineer Overview We are looking for a talented Senior AI Engineer to work with our Foundry Research and Development team to build innovative products delivered at scale to global markets. Role Contributes to the design and development of scalable AI/ML systems and solutions to address complex business needs, ensuring adherence to best practices. Implements models into production environments, designing scalable training pipelines and deployment frameworks. Ensures operational stability and scalability of AI systems, contributing to the organizations AI infrastructure and ethical standards. Automates workflows for model training, testing, deployment, and updates, following CI/CD best practices. Assists with the building and refining of data ingestion, preprocessing, and feature engineering workflows to support model training and inference. Conducts hyperparameter tuning and validation to meet targeted performance metrics, ensuring robustness and efficiency for AI/ML models. Monitors model performance, manages versioning, and updates models to maintain high-quality outputs. May contribute to solution development for new products/services and/or manage smaller project/initiatives as an experienced individual contributor with specialized knowledge within the AI Engineering area. All About You: Multiple years of relevant experience in AI and Data Science. Enjoy building innovative solutions in a collaborative, fast-paced environment. Possess advanced knowledge of modern software engineering principles and methodologies. Be passionate about code quality and software engineering best practices. Demonstrate initiative and a willingness to take on complex, challenging problems. Exhibit excellent verbal and written communication skills with strong collaboration abilities. Be highly motivated, driven, and a strong team player. Be able to work independently, make sound decisions, and solve problems with minimal supervision. Skills Strong experience building Agentic AI applications using frameworks such as LangGraph, CrewAI, and AutoGen, with solid understanding of Agentic AI design patterns, Context Management, LLMOps, AgentOps, Guardrails, Agent Validation, and Evaluation. Hands-on experience with prompt engineering and working with both closed-source and open-source LLMs. Experience applying RAG, few-shot prompting, LLM fine-tuning, and hybrid approaches to improve model context and performance. Good working knowledge of MLOps tools, including MLflow. Hands-on experience with LLM fine-tuning techniques. Strong experience with Retrieval Augmented Generation (RAG), vector databases, and semantic search. Proven experience designing and maintaining CI/CD pipelines to automate integration, testing, and deployment, ensuring reliable and high-velocity delivery. Proficiency in Python and the data science ecosystem, including NumPy, pandas, sklearn, spaCy, Keras, PyTorch, Transformers, and LangGraph. Strong understanding of Machine Learning, Deep Learning, and NLP concepts and models across supervised and unsupervised learning. Working knowledge of Python-based API frameworks such as FastAPI, with comfort handling JSON-based services. Thorough understanding of PySpark with a conceptual understanding of parallel and distributed processing for large-scale data. Experience using Unix/Linux commands to access systems, manage databases, and deploy and operate services and APIs. Working knowledge of cloud platforms such as Azure and experience using cloud-native services. Working knowledge of Databricks is a plus. \\#AI1 Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable\\ and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly. \\\\Corporate Security Responsibility\\\\ All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: \\+ Abide by Mastercards security policies and practices; \\+ Ensure the confidentiality and integrity of the information being accessed; \\+ Report any suspected information security violation or breach, and \\+ Complete all periodic mandatory security trainings in accordance with Mastercards guidelines. In line with Mastercards total compensation philosophy and assuming that the job will be performed in Canada, the successful candidate will be offered a competitive pay based on location, experience and other qualifications for the role and may be eligible to participate in a discretionary annual incentive program. This posting reflects one or more current openings on our team. \\\\Pay Ranges\\\\ Toronto, Canada: $83,000 - $132,000 CAD

The role requires multiple years of relevant experience in AI and Data Science. Candidates should possess advanced knowledge of modern software engineering principles and methodologies and be passionate about code quality and software engineering best practices. Strong experience is needed in building Agentic AI applications using frameworks like LangGraph, CrewAI, and AutoGen, with a solid understanding of Agentic AI design patterns, Context Management, LLMOps, AgentOps, Guardrails, Agent Validation, and Evaluation. Experience with prompt engineering, working with both closed-source and open-source LLMs, and applying techniques such as RAG, few-shot prompting, and LLM fine-tuning is also required. Proficiency in Python and its data science ecosystem, including NumPy, pandas, sklearn, spaCy, Keras, PyTorch, and Transformers, is necessary. A strong understanding of Machine Learning, Deep Learning, and NLP concepts and models is essential, along with working knowledge of Python-based API frameworks like FastAPI. Experience with CI/CD pipelines for automation and cloud platforms such as Azure is also a requirement.