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Lead AI Engineer in Singapore building next-gen agentic AI platforms at Mastercard.
Drive AI platform development, governance, and innovation across enterprise.
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
Lead AI EngineerOverview As a Lead Agentic AI Engineer on the AI Centre of Excellence team, you will play an important role in designing, building, and operationalising Mastercard's next-generation AI and agentic systems. You will combine deep software engineering expertise with strong knowledge of AI systems, orchestration frameworks, and cloud-native architectures to build scalable solutions that enable teams across Mastercard to safely deploy and govern AI-powered applications. You will lead the development of agentic AI capabilities spanning orchestration, evaluation, observability, governance, and runtime execution while working closely with product teams, data scientists, platform engineers, and business stakeholders. Your work will directly influence how AI solutions are built, governed, and operated across the enterprise.
Your Role
- • End-to-End Platform Development: Lead the full lifecycle development of agentic AI platforms and services, from architecture and design through implementation, deployment, and operational support.
- • Agentic AI Engineering: Design and develop agentic systems, including agent orchestration frameworks, tool integrations, workflow engines, model routing services, memory management capabilities, and multi-agent coordination patterns.
- • AI Platform Services: Build scalable platform components that support AI application development, including evaluation services, governance controls, policy management, observability capabilities, and operational tooling.
- • Backend Engineering: Develop robust backend services, APIs, and event-driven architectures that enable enterprise-scale AI workloads, real-time orchestration, and secure platform operations.
- • API & Developer Experience: Design and implement API-first platform capabilities, SDKs, and reusable frameworks that simplify the development, deployment, and governance of AI applications.
- • Performance & Reliability: Identify and resolve bottlenecks across AI systems and agentic workflows, improving scalability, resilience, latency, cost efficiency, and operational reliability.
- • Technical Leadership: Provide technical leadership through architecture reviews, code reviews, mentoring, and engineering best practices. Champion high standards for software quality, reliability, maintainability, and production readiness.
- • Cross-Functional Collaboration: Work closely with AI engineers, data scientists, platform teams, product managers, AI governance teams, security teams, and business stakeholders to translate strategic objectives into scalable technical solutions.
- • AI Governance & Trust: Partner with governance, privacy, risk, and security teams to implement responsible AI controls, evaluation frameworks, policy enforcement mechanisms, and monitoring capabilities.
- • Innovation & Continuous Improvement: Stay at the forefront of advancements in agentic AI, orchestration frameworks, LLM technologies, and AI engineering practices, continuously identifying opportunities to improve platform capabilities and developer productivity.
All About You
- • Minimum 8+ years of software engineering experience, including significant hands-on experience building and deploying AI/ML-powered applications and platforms.
- • Proven track record of delivering production-grade AI systems, agentic applications, or platform services at enterprise scale.
- • Strong software engineering fundamentals with demonstrated experience building scalable, resilient, and maintainable distributed systems.
- • Hands-on experience developing agentic workflows, orchestration frameworks, tool-calling architectures, or AI-powered automation solutions.
- • Strong proficiency in Python and modern software engineering practices, including testing, CI/CD, observability, and cloud-native development.
- • Experience with agentic AI frameworks and orchestration technologies such as LangGraph, LangChain, CrewAI, MCP-based architectures, or equivalent solutions.
- • Experience building APIs, backend services, event-driven systems, and microservices supporting AI applications and platform capabilities.
- • Familiarity with cloud platforms and modern infrastructure technologies such as AWS, Databricks, Kubernetes, and containerized deployments.
- • Understanding of LLM application development, retrieval-augmented generation (RAG), evaluation methodologies, guardrails, and responsible AI practices.
- • Familiarity with data architectures including data lakes, lakehouse platforms, vector databases, and AI data pipelines.
- • Strong collaboration and stakeholder management skills, with the ability to explain technical concepts to both engineering and business audiences.
- • Proven ability to lead technical initiatives, mentor engineers, and foster a culture of engineering excellence.
The Following Skills Are Preferred
- • Experience building enterprise AI platforms, developer platforms, or internal AI tooling.
- • Experience implementing AI governance, evaluation, observability, or monitoring capabilities.
- • Experience with multi-agent systems and agent-to-agent communication architectures.
- • Experience in financial services, payments, or highly regulated industries.
- • Experience working with large-scale enterprise platform engineering teams.
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.