Senior AI Engineer job opportunity at MasterCard.

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DatePosted 2 Days Ago bot
MasterCard Senior AI Engineer
Experience: General
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 Senior AI Engineer Overview As a Senior AI Engineer on the AI Foundations team, you will independently execute key elements of AI engineering projects and operational processes, applying deep technical expertise and AI engineering best practices to resolve problems and remove roadblocks as they arise. You will contribute to the design, development, and productionization of scalable AI/ML systems that address complex business needs, with a strong focus on reliability, observability, and responsible AI. This role is an experienced individual contributor position, partnering closely with data scientists, product, and platform teams to deliver end-to-end AI capabilities from data/feature pipelines through deployment and lifecycle operations. Responsibilities Independently deliver project components within the AI Engineering area, applying in-depth discipline knowledge and established best practices to resolve issues and unblock delivery. Design and develop scalable AI/ML systems and solutions to address complex business needs, ensuring alignment with engineering standards and AI best practices. Productionize models and AI services by implementing models into production environments and designing scalable training pipelines and deployment frameworks. Build and refine data workflows for data ingestion, preprocessing, and feature engineering to support training and inference, including structured and unstructured/multimodal sources where applicable. Feature engineering & vector/feature store enablement: develop robust pipelines that transform raw data into reusable features/embeddings suitable for AI training, testing, and inference at scale. Hyperparameter tuning and validation to meet target performance metrics, ensuring models are accurate, robust, and efficient. Automate AI delivery workflows for training, testing, deployment, and updates using CI/CD best practices (MLOps), improving repeatability and time-to-production. Model serving, scaling & observability: deploy AI models into production with appropriate monitoring, logging, tracing, and operational dashboards to ensure performance and reliability at scale. Drift detection & telemetry: monitor for data drift and concept drift, track performance decay, and trigger remediation actions (investigation, retraining, rollback, or guardrail updates). Lifecycle management: monitor model performance, manage model/version registries, and update models to maintain high-quality outputs over time. AI model retraining automation: automate retraining and redeployment pipelines; evaluate and apply approaches such as fine-tuning, RAG, and guardrails to maintain accuracy and safety. Operational stability & responsible AI: ensure system scalability and stability while adhering to ethical guidelines and contributing to broader AI infrastructure standards. Contribute to solution development for new products/services and/or lead smaller initiatives as an experienced IC with specialized AI engineering expertise. Mentor and uplift engineering quality by reviewing designs/code, sharing best practices, and guiding junior engineers through on-the-job coaching. Key Skills AI Engineering & MLOps Proven experience operationalizing AI/ML models end-to-end: training pipelines, deployment frameworks, versioning, monitoring, and continuous improvement. CI/CD for ML systems (testing automation, release pipelines, reproducibility, model registry/version management). Model performance monitoring, drift detection approaches, telemetry/observability patterns, and incident triage for AI services. Data & Feature Engineering Building data ingestion, preprocessing, and feature engineering workflows to support training and inference (batch and/or streaming). Experience with feature pipelines and embedding/vector workflows (e.g., feature stores/vector stores) for scalable AI applications Model Development & Optimization Strong fundamentals in ML model evaluation, hyperparameter tuning, validation strategies, robustness, and efficiency. Practical understanding of modern techniques such as fine-tuning and RAG patterns for applied AI systems. Production Systems & Software Engineering Building production-grade services/APIs that integrate AI workflows (training, inference, orchestration) with scalable backend engineering practices. Solid software engineering fundamentals: clean architecture, code quality, automated tests, performance optimization, and reliability engineering. Collaboration & Delivery Ability to independently drive deliverables, resolve ambiguity, and partner cross-functionally with product, data science, and platform teams to translate business needs into scalable solutions. Comfortable contributing to smaller initiatives end-to-end and influencing engineering standards through reviews and mentorship 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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