Role Description We are looking for a Senior AI Engineer to help build the core intelligence layer behind TaxGPT. This role is focused on improving how effectively, reliably, and safely AI can automate real tax workflows using LLMs, retrieval systems, structured context, and agentic workflows. You will work closely with software engineers, product teams, and technical leadership to design, build and scale AI systems that support fast development, stable production environments, and long-term scalability. This is a senior individual contributor role for someone who can work with high autonomy, move quickly from prototype to production, and bring strong judgment to applied AI product development. What You'll Do - AI Systems and Agentic Workflows - Build and improve AI-powered product experiences for tax and accounting workflows - Increase automation rates across workflows while maintaining accuracy, reliability, and a strong user experience - Design and refine prompt-based systems, tool-using agents, and multi-step workflows that perform well in real production use cases - Prototype and ship end-to-end AI features quickly, from idea to working product - Design workflows that combine LLMs, tools, APIs, retrieval systems, and structured outputs in a robust way - Work across APIs, tools, retrieval layers, and backend systems to make AI capabilities useful in real user workflows - Model Quality, Evals, and AI Reliability - Build and maintain evals, regression tests, and benchmarks that help us measure and improve AI quality over time - Define practical metrics for usefulness, accuracy, latency, reliability, and cost - Investigate model failures and systematically improve performance through better prompting, context design, routing, and system architecture - Contribute to fine-tuning experiments, benchmark design, and dataset development where it adds product value - Applied Engineering & Technical Leadership - Write strong Python code for prototypes, internal tooling, backend services, and AI workflows - Work with product and backend engineers to productionize AI systems cleanly and safely - Use data and experimentation to guide decisions, validate improvements, and prioritize the highest-impact work - Help build feedback loops that make AI behavior easier to understand, debug, and improve over time - Make strong technical decisions in your area and help the team balance speed, quality, and maintainability - Share clear guidance through code reviews, design discussions, and documentation Qualifications - 5+ years of experience in software engineering, machine learning engineering, AI engineering or a related role - Strong experience building with LLMs in real products, not just experimentation environments - Experience designing and improving prompt-based systems to achieve reliable, high-quality outcomes - Strong experience with agentic workflows, tool-using AI systems, or multi-step AI orchestration - Experience building evals, regression tests, benchmarks, and monitoring for AI workflows - Strong understanding of tradeoffs in model quality, latency, cost, reliability, and system design - Strong scripting or coding ability in languages such as Python, Go, or TypeScript - Comfort working with APIs, backend systems, data flows, and cloud environments - Strong written and verbal communication skills Preferred Qualifications - Experience supporting fast-moving startup engineering teams - Experience building products from 0 to 1 - Experience with fine-tuning LLMs, synthetic dataset creation, or benchmark development - Experience working with sensitive or regulated data domains such as tax, accounting, finance, legal, or healthcare - Familiarity with modern AI evaluation and observability tooling How We Define Success in This Role - A strong Senior AI Engineer in this role: - Ships AI features that create real user value - Improves automation, accuracy, and reliability across important workflows - Operates with high autonomy and strong product judgment - Moves quickly without sacrificing reliability or sound engineering practices - Helps the team make better decisions about how to build and evaluate AI systems Stack Context - TaxGPT's core stack: Django, FastAPI, React / Next.js, Go, AWS EKS - Recent expansion into Azure and GCP - Kubernetes via Porter for deployment - PostgreSQL - GitHub for source control Benefits - Medical, dental, vision, 401k + 3% match, life insurance

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're 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 Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realise their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview The CNPF Data & AI organisation is looking for a Senior AI Engineer to contribute hands-on to the delivery of applied AI and agentic capabilities across our platforms. This role sits at the intersection of software engineering, machine learning engineering, and applied data science, with a strong focus on building and operating production-grade AI systems. This is a senior individual contributor role. You will work closely with Applied AI, Data Science, and Product teams to help take AI solutions from experimentation through to secure, scalable production - bringing strong engineering rigour and a collaborative mindset to everything you build. Role Develop and contribute to AI and agentic systems across the full lifecycle from design through production deployment Build and operate ML/AI services, pipelines and APIs using strong software engineering practices Implement ML engineering capabilities including model serving, monitoring, evaluation and retraining Partner with data scientists to productionise models and experiments efficiently Contribute to data preparation, feature engineering, experimentation and modelling as needed Participate in technical design reviews and support knowledge sharing across engineering and data science teams Ensure AI solutions meet Mastercard standards for performance, reliability, security and governance Collaborate with platform, security, and infrastructure teams to ship responsibly at scale All about you Solid experience as a hands-on AI engineer, ML engineer, or software engineer working on production AI systems Strong foundations in software engineering, system design, and distributed systems Practical experience productionising machine learning models and supporting their operation at scale Comfortable working across data engineering, ML engineering, and applied data science tasks Familiarity with large-scale data platforms and modern ML/AI tooling Good problem-solving skills with the ability to navigate ambiguous requirements Collaborative and communicative, able to work effectively across functions and disciplines What Makes You Stand Out You have contributed to AI or agentic applications running in real production environments Hands-on experience with agent-based or LLM-powered systems beyond simple POCs Good instincts for reliability, observability, and failure handling in AI systems Ability to move between engineering execution and applied modelling depending on what the problem needs Eagerness to grow technically and contribute positively to the engineering culture around you Corporate Security Responsibility Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organisation and therefore it is expected that the successful candidate must:• Abide by Mastercard's security policies and practices• Ensure the confidentiality and integrity of the information being accessed• Report any suspected information security violation or breach• Complete all mandatory security trainings in accordance with Mastercard's guidelines 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 Mastercard's 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 Mastercard's guidelines.

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're 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 Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realise their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview The CNPF Data & AI organisation is looking for a Senior AI Engineer to contribute hands-on to the delivery of applied AI and agentic capabilities across our platforms. This role sits at the intersection of software engineering, machine learning engineering, and applied data science, with a strong focus on building and operating production-grade AI systems. This is a senior individual contributor role. You will work closely with Applied AI, Data Science, and Product teams to help take AI solutions from experimentation through to secure, scalable production - bringing strong engineering rigour and a collaborative mindset to everything you build. Role Develop and contribute to AI and agentic systems across the full lifecycle from design through production deployment Build and operate ML/AI services, pipelines and APIs using strong software engineering practices Implement ML engineering capabilities including model serving, monitoring, evaluation and retraining Partner with data scientists to productionise models and experiments efficiently Contribute to data preparation, feature engineering, experimentation and modelling as needed Participate in technical design reviews and support knowledge sharing across engineering and data science teams Ensure AI solutions meet Mastercard standards for performance, reliability, security and governance Collaborate with platform, security, and infrastructure teams to ship responsibly at scale All about you Solid experience as a hands-on AI engineer, ML engineer, or software engineer working on production AI systems Strong foundations in software engineering, system design, and distributed systems Practical experience productionising machine learning models and supporting their operation at scale Comfortable working across data engineering, ML engineering, and applied data science tasks Familiarity with large-scale data platforms and modern ML/AI tooling Good problem-solving skills with the ability to navigate ambiguous requirements Collaborative and communicative, able to work effectively across functions and disciplines What Makes You Stand Out You have contributed to AI or agentic applications running in real production environments Hands-on experience with agent-based or LLM-powered systems beyond simple POCs Good instincts for reliability, observability, and failure handling in AI systems Ability to move between engineering execution and applied modelling depending on what the problem needs Eagerness to grow technically and contribute positively to the engineering culture around you Corporate Security Responsibility Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organisation and therefore it is expected that the successful candidate must:• Abide by Mastercard's security policies and practices• Ensure the confidentiality and integrity of the information being accessed• Report any suspected information security violation or breach• Complete all mandatory security trainings in accordance with Mastercard's guidelines 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 Mastercard's 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 Mastercard's guidelines.

• Build foundation models and generative AI tools alongside a team of technologists. • Design and build agentic workflows — multi-agent orchestration (e.g., CrewAI, LangGraph, AutoGen), tool use, multi-step planning, and human-in-the-loop checkpoints — to automate complex engineering tasks. • Establish evaluation, guardrails, and failure-mode analysis for agent systems to ensure they are safe, reliable, and production-ready. • Develop scalable data pipelines for diverse data sources used in production ML systems, including BIM, CAD, and infrastructure design data. • Work with large-scale, multi-modal datasets — including text and geometric data — to design novel preprocessing, augmentation, analysis, and content understanding. • Transform unstructured infrastructure and design data into representations suitable for machine learning. • Lead cross-functional collaboration with ML Research Scientists and Engineers to align data formats with downstream training and fine-tuning of LLMs. • Apply deduplication, normalization, and validation techniques to ensure high-quality data in production environments. • Architect and optimize pipelines for scalability, reproducibility, and cloud deployment. • Mentor junior engineers and provide technical guidance on complex data challenges. • Drive technical decision-making and influence best practices across the team. • Perform requirements analysis with senior stakeholders, ensuring technical solutions meet both immediate project goals and long-term research objectives. • Communicate findings and technical insights through quantitative analysis, visualizations, and clear documentation. • Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs. • Participate in technical planning and roadmap development.