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- 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 4+ years in AI/ML applications required.
- Other info: Mentorship opportunities available for junior engineers to grow their skills.
The predicted salary is between 57600 - 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 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 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:
- 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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Lead AI Engineer employer: Mastercard, Inc.
Contact Detail:
Mastercard, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead AI Engineer
✨Tip Number 1
Familiarise yourself with Mastercard's AI initiatives and the specific technologies they use. This will not only help you understand their needs better but also allow you to tailor your discussions during interviews to show how your experience aligns with their projects.
✨Tip Number 2
Network with current or former employees of Mastercard, especially those in AI roles. They can provide valuable insights into the company culture and expectations, which can be crucial for your interview preparation.
✨Tip Number 3
Prepare to discuss specific examples of how you've successfully transitioned AI models from prototypes to production. Highlight any challenges you faced and how you overcame them, as this will demonstrate your problem-solving skills and technical expertise.
✨Tip Number 4
Stay updated on the latest trends in AI and microservices architecture. Being able to discuss recent advancements or methodologies can set you apart and show your commitment to continuous learning in this fast-evolving field.
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 include specific examples that demonstrate your technical skills and leadership abilities.
Craft a Compelling Cover Letter: Write a cover letter that connects your background to Mastercard's mission. Emphasise your passion for AI technology and how your experience aligns with the responsibilities of the Lead AI Engineer role. Be sure to mention any relevant projects or technologies you've worked with.
Showcase Technical Skills: In your application, clearly outline your technical skills, especially in Python, RESTful APIs, and cloud platforms. Mention any experience with containerization technologies and CI/CD pipelines, as these are crucial for the role.
Highlight Leadership Experience: Since this is a lead position, it's important to showcase any previous leadership roles or mentoring experiences. Discuss how you've guided teams or influenced projects, particularly in fast-paced environments, to demonstrate your capability to lead at Mastercard.
How to prepare for a job interview at Mastercard, Inc.
✨Showcase Your Technical Expertise
As a Lead AI Engineer, you'll need to demonstrate your strong proficiency in Python and experience with RESTful APIs. Be prepared to discuss specific projects where you've designed and deployed AI microservices, highlighting the challenges you faced and how you overcame them.
✨Understand the AI/ML Lifecycle
Make sure you can articulate the entire AI/ML lifecycle, from data preparation to deployment and monitoring. Discuss your experience with scaling machine learning models and any familiarity you have with feature stores or model registries, as this will be crucial for the role.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your problem-solving skills. Think about potential performance bottlenecks in AI microservices and how you would address them. Being able to provide concrete examples will show your ability to think critically and strategically.
✨Emphasise Collaboration and Mentorship
Highlight your experience working closely with data scientists, MLOps engineers, and product teams. Discuss how you've mentored junior engineers on best practices for AI software development, as collaboration is key in this role at Mastercard.
