MastercardLead Software Engineer
Mastercard is hiring a Lead Software Engineer in Dublin, Ireland. Level rates it ; you can apply on Mastercard.
Level's read on this job
- Builds AI ●●●● is how Level rates this software engineer role: the job is building AI systems.
- The posting mentions Databricks, Python and Java among its tools and skills.
- Level classes it as a lead role.
- Mastercard has 1,000+ other open roles on Level.
AI in this role
Lead Software Engineer to architect and deploy AI-powered products, intelligent agents, and LLM workflows at Mastercard.
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 Software EngineerWho is Mastercard?Mastercard is the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, and a technology innovation lab. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you.
Position Responsibilities:
At Mastercard Services, you will serve as a senior technical leader and hands-on engineer for BE&O’s next-generation experimentation and analytics platform. As a Lead Software Engineer, you will lead through technical influence, architectural judgment, and execution excellence to help modernize our platform and deliver scalable, secure, cloud-native capabilities for global business users.
Design, develop, and deploy AI-powered products, intelligent agents, and agentic workflows utilizing Large Language Models (LLMs), retrieval systems, orchestration frameworks, tool-calling patterns, and context-aware workflows.
Architect and implement advanced agentic systems incorporating planning, reasoning loops, memory management, grounding, evaluation frameworks, safety mechanisms, and human-in-the-loop controls.
Lead the transition of AI solutions from proof-of-concept to production by establishing best practices for reliability, observability, performance, security, governance, and cost optimization.
Develop scalable AI applications and developer productivity solutions using modern AI frameworks, SDKs, and orchestration platforms.
Serve as a hands-on technical contributor, designing, coding, testing, debugging, and delivering production-quality software across the full engineering lifecycle.
Build and evolve scalable services, APIs, and analytical capabilities using modern engineering practices, cloud platforms, and data technologies such as AWS, Azure, Databricks, Spark/PySpark, Python, Java and related frameworks.
Partner closely with product managers, engineering teams, architects, data scientists, QA, security, and operations teams to translate business and functional requirements into robust technical solutions.
Coordinate and lead technical execution across globally distributed engineering teams, ensuring alignment across regions, time zones, dependencies, design decisions, and delivery milestones.
Drive architecture and design reviews, code reviews, technical decision-making, and engineering tradeoff discussions to ensure solutions are secure, resilient, observable, performant, maintainable, and aligned with enterprise standards.
Drive engineering excellence through CI/CD automation, DevSecOps practices, automated testing, and AI-assisted software development workflows.
Establish reusable platforms, frameworks, accelerators, and engineering capabilities that improve developer productivity and accelerate solution delivery across teams.
Identify opportunities to improve platform scalability, reliability, developer productivity, operational readiness, and speed of delivery through modernization, automation, reusable patterns, and engineering best practices.
Provide technical mentorship to junior and mid-level engineers through design guidance, code review, pairing, knowledge sharing, and modeling strong engineering judgment.
Navigate ambiguity in complex initiatives by clarifying technical direction, decomposing large problems, identifying risks, and driving alignment across stakeholders.
Champion a culture of engineering excellence, continuous improvement, ownership, collaboration, and high-quality delivery across the team and partner organizations.
Ideal Candidate Qualifications:
8+ years of software engineering experience delivering scalable, high-performance, and reliable software in an agile environment.
Strong hands-on experience designing and building distributed systems, microservices, RESTful APIs, and cloud-native applications.
Strong experience with data-intensive applications and analytics platforms; experience with Databricks, Spark, or PySpark is highly desirable.
Proven experience building and delivering AI-powered applications, intelligent agents, or generative AI solutions in enterprise environments.
Strong understanding of agentic AI architectures, including workflow orchestration, reasoning frameworks, memory systems, grounding techniques, evaluation methodologies, and safety controls.
Hands-on experience with modern AI frameworks, SDKs, orchestration platforms, vector databases, retrieval systems, and agent development tooling.
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.
How we rate this
Lead Software Engineer at Mastercard rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in software engineer jobs at this level
Pay
- Typical software engineer job
- $168k
This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where llms was part of your work. What did you do?
- Tell me about a project where system architecture was part of your work. What did you do?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
Adapt your resume
- List these exact terms on your resume: AI agents, Machine learning, LLMs, System Architecture, and NLP. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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