At a Glance
- Tasks: Lead the design and development of AI microservices for Mastercard's innovative solutions.
- Company: Join Mastercard, a global leader in digital payments, empowering economies in over 200 countries.
- Benefits: Enjoy competitive pay, remote work options, and a vibrant corporate culture.
- Why this job: Be at the forefront of AI technology, making a real impact on global transactions.
- Qualifications: 8+ years in software development, with strong skills in Python and AI/ML applications.
- Other info: Mentorship opportunities available for junior engineers and a focus on ethical AI practices.
The predicted salary is between 54000 - 84000 £ per year.
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build asustainableeconomy where everyone can prosper. We support a wide range of digital payments choices, making transactionssecure, 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 Engineer Overview
As a Lead AI Engineer at Mastercard, you will be instrumental in designing, building, and scaling AI microservices that transform PoC notebooks into robust, production-ready components. You will play a pivotal role in creating the foundational AI services that power our next-generation solutions, ensuring they are scalable, efficient, and seamlessly integrated into the broader Mastercard ecosystem
Key Responsibilities
Lead the E2E design and development of AI-powered microservices, focusing on modularity, scalability, and reusability, to support various business units and applications
Drive the transition of AI models from notebook-based PoCs and experimental phases into hardened, production-grade components and services, ensuring performance, reliability, and maintainability
Design and implement robust APIs for AI microservices, facilitating seamless integration with existing Mastercard platforms and external systems.
Identify and address performance bottlenecks within AI microservices and their underlying infrastructure, optimizing for latency, throughput, and cost-efficiency.
Work closely with data scientists, MLOps engineers, and product teams to translate business requirements into technical specifications for AI services. Mentor junior engineers on best practices for AI software development and scaling
Implement rigorous testing strategies, including unit, integration, and performance testing, to ensure the quality, accuracy, and stability of deployed AI microservices
Research and evaluate new technologies, frameworks, and methodologies related to AI software development, microservices, and large-scale data processing to continuously improve our capabilities
Ensure all AI microservices adhere to Mastercard\’s security standards, compliance policies, and ethical AI principles
Qualifications
Education: Bachelor\’s degree in Computer Science, Engineering, Data Science, or a related technical field. Master\’s degree preferred
Experience: Minimum of 8+ years of experience in software development, with at least 4 years specifically focused on building and deploying AI/ML-powered applications or microservices in production environments
Technical Skills:
Strong proficiency in Python
Extensive experience in designing, developing, and deploying RESTful APIs and microservices
Proven experience in scaling machine learning models from prototype to production, including familiarity with feature stores, model registries, and inference patterns
Solid understanding of the AI/ML lifecycle, from data preparation and model training to deployment and monitoring
Experience with cloud platforms and their relevant compute, storage, and AI/ML services
Proficiency with containerization technologies
Familiarity with CI/CD pipelines for automated testing and deployment of software and AI models
Experience with distributed computing frameworks (e.g., Spark, Dask) is a plus
Understanding of data governance, data quality, and data security principles relevant to AI/ML applications
Excellent communication, interpersonal, and stakeholder management skills, with the ability to effectively articulate complex technical concepts to both technical and non-technical audiences
Proven ability to lead technical initiatives, drive cross-functional projects, and influence outcomes within a fast-paced environment
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:
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Abide by Mastercard’s security policies and practices;
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Ensure the confidentiality and integrity of the information being accessed;
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Report any suspected information security violation or breach, and
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Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
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Lead AI Engineer employer: MasterCard
Contact Detail:
MasterCard Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead AI Engineer
✨Tip Number 1
Familiarise yourself with Mastercard's core values and mission. Understanding how they empower economies and people will help you align your responses during interviews, showcasing your passion for their purpose.
✨Tip Number 2
Network with current or former employees on platforms like LinkedIn. Engaging in conversations about their experiences can provide valuable insights into the company culture and expectations for the Lead AI Engineer role.
✨Tip Number 3
Stay updated on the latest trends in AI and microservices. Being able to discuss recent advancements or technologies during your interview will demonstrate your commitment to continuous learning and innovation.
✨Tip Number 4
Prepare to discuss specific projects where you've successfully transitioned AI models from prototype to production. Highlighting your hands-on experience will show that you have the practical skills needed for the role.
We think you need these skills to ace Lead AI Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI and microservices. Focus on your achievements in building and deploying AI/ML applications, and ensure you mention specific technologies and methodologies you've used.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how your skills align with Mastercard's mission. Mention specific projects where you've led the design and development of AI solutions, and how they relate to the responsibilities outlined in the job description.
Showcase Technical Skills: Clearly list your technical skills related to Python, RESTful APIs, and cloud platforms. Provide examples of how you've applied these skills in previous roles, particularly in scaling machine learning models and working with containerization technologies.
Highlight Leadership Experience: Since this is a lead position, emphasise any leadership roles you've held. Discuss your experience mentoring junior engineers and leading cross-functional projects, showcasing your ability to drive technical initiatives and influence outcomes.
How to prepare for a job interview at MasterCard
✨Showcase Your Technical Expertise
As a Lead AI Engineer, it's crucial to demonstrate your strong proficiency in Python and your experience with RESTful APIs. Be prepared to discuss specific projects where you've built and deployed AI/ML applications, highlighting the challenges you faced and how you overcame them.
✨Understand Mastercard's Ecosystem
Familiarise yourself with Mastercard's products and services, especially those related to AI and digital payments. This knowledge will help you articulate how your skills can contribute to their mission of building a sustainable economy and enhancing transaction security.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities, particularly around scaling AI models and optimising microservices. Think of examples from your past experiences where you successfully addressed performance bottlenecks or improved system efficiency.
✨Emphasise Collaboration and Mentorship
Highlight your experience working with cross-functional teams, including data scientists and MLOps engineers. Discuss how you've mentored junior engineers and contributed to a collaborative environment, as this aligns with the role's responsibilities at Mastercard.