Principal Software Engineer (AI Solution Architect) job opportunity at MasterCard.

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DateMore Than 30 Days Ago bot
MasterCard Principal Software Engineer (AI Solution Architect)
Experience: 15-years
Pattern: full-time
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loacation Singapore, Singapore
loacation Singapore....Singapore

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 Principal Software Engineer (AI Solution Architect) Principal Software Engineer (AI Solution Architect) - Foundry R&D, Singapore We are looking for a Principal Software Engineer (AI Solution Architect) to join Mastercard Foundry R&D. You will help drive innovation by exploring new technologies and shaping scalable architectures for AI‑powered solutions. The ideal candidate is hands‑on, curious, analytical, and comfortable working in fast‑moving R&D environments. What you'll do * Architect scalable solutions: Design lightweight, modular architectures that support rapid experimentation and can scale toward production. Build systems with clear separation of components, reusable patterns, and flexible deployment paths across cloud environments. * Architect AI solutions: Lead end‑to‑end AI solution design, from understanding requirements to defining data flows, model selection, integration patterns, and technical blueprints. Balance innovation with practical considerations such as performance, cost, and security. * Define best practices and governance: Establish standards for AI development, infrastructure choices, and data pipelines. Apply Responsible AI guidelines, including data handling, fairness considerations, guardrails, and safety controls. Publish reference architectures, templates, and guidance to support adoption across teams. * Cross-functional collaboration: Work with product managers, analysts, engineers, and customers to translate business needs into sound technical designs. Explain architectural decisions clearly and prepare documentation, presentations, and demos for both technical and non‑technical stakeholders. * Technical leadership in delivery: Support engineering teams through design reviews, troubleshooting, and iterative development. Provide guidance to resolve integration challenges and help convert architectural plans into actionable tasks. Contribute to prototypes where needed. * Product transition and scaling: Lead efforts to harden prototypes into reliable products by improving robustness, security, performance, and compliance. Collaborate with platform teams to deploy solutions at scale. * Stay current on AI: Track emerging AI tools, models, and frameworks. Evaluate new techniques, lead experiments, and guide the team in applying modern approaches such as new LLM capabilities or fine‑tuning methods. What you'll bring * Extensive software engineering experience: 15+ years building complex software systems across front‑end, backend, and distributed components. Ability to dive into any part of a system and make informed architectural decisions. * Solution architecture experience: 5+ years translating requirements into scalable architectures involving multiple components, integrations, and services. Familiarity with distributed systems, performance optimization, and security. * Deep AI and ML expertise: Strong understanding of generative AI, large language models, and traditional ML. Hands‑on experience designing and integrating AI systems and selecting appropriate modeling approaches for various problems. * AI integration experience: Practical experience deploying AI models into real applications, such as NLP systems or computer vision. Familiar with frameworks, SDKs, and tools that support AI workflows and prompt‑based systems. * Full-stack and DevOps proficiency: Strong programming skills in languages such as Python and Java/Node.js. Experience with cloud platforms, containers, orchestration, monitoring, and CI/CD pipelines. Stakeholder management and communication: Able to explain complex concepts simply and collaborate with diverse teams. Skilled at documentation, architecture diagrams, and communicating trade-offs. * Project leadership: Experience leading technical workstreams, mentoring teams, and solving challenging problems in agile environments. Able to drive consensus and support continual process improvement. * Continuous learning: Active interest in new AI research, tools, and architectural trends. Participation in tech communities or creating thought leadership content is a plus. Required skills * Education and background: Bachelor's or Master’s degree in Computer Science or related field. Strong computer science fundamentals with a track record of architecting and optimizing high‑performance systems. * Software development mastery: Advanced coding skills in languages such as Python, Java, C#, or JavaScript/TypeScript. Experience across full-stack development, building APIs, managing data, and developing maintainable, tested code. * AI and ML knowledge: Hands‑on experience with ML lifecycles, from data preparation through deployment. Familiarity with generative AI, embeddings, vector databases, and prompt engineering. * Systems and cloud architecture: Experience designing distributed systems and cloud architectures on AWS, Azure, or GCP. Skilled with patterns like microservices, event‑driven systems, message queues, and API design. * Generative AI domain knowledge: Familiar with LLMs, multimodal models, and related frameworks. Understanding of limitations such as hallucinations and methods to mitigate them. * Analytical and problem-solving skills: Ability to evaluate technical options, run experiments, and make trade‑off decisions. Skilled at prototyping to validate ideas quickly. * Methodologies and standards: Experience with agile development, DevOps/MLOps practices, CI/CD pipelines, containerization, monitoring, and architectural decision records. * Communication and collaboration: Able to work with designers, engineers, and product teams. Strong documentation and teamwork skills. Preferred skills * Advanced education and certifications: Master's or PhD in AI/ML or cloud/architecture certifications. * Expertise in the generative AI ecosystem: Experience with frameworks like Hugging Face Transformers, LangChain, DeepSpeed, or Megatron‑LM. * Scalable AI systems and MLOps: Experience with large-scale model training, distributed processing, GPU/TPU optimization, and MLOps tools such as MLflow, Kubeflow, or SageMaker. * Domain experience: Background in fintech, payments, or enterprise software. Understanding of regulations and security considerations. * Leadership in innovation: Experience as a staff-level engineer or architect, driving innovation, filing patents, or contributing to open source. * Certifications: Cloud or AI-related certifications showcasing technical depth. * LLM and NLP specialization: Experience training or fine‑tuning language models, understanding transformer internals, and working with NLP evaluation metrics. Contributions to research or the open-source AI community are valued. 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.

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