Mastercard
Gen AI Platform Principal Software Engineer
Mastercard, New York, New York, us, 10261
Our Purpose
We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a
culture of inclusion
for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.
Title and SummaryGen AI Platform Principal Software Engineer
OverviewThe Mastercard Cloud Team, in Mastercard’s ONE division, owns and drives cloud usage and adoption for the company. The team has a unique opportunity to drive cloud patterns, standards, and best practices across all of Mastercard and their M&A’s. This cloud engineering role is focused on building a holistic platform for Generative AI Large Language Models (LLMs).
Role
Design, configure, and implement a Gen. AI platform, including MLOps/LLMOps for Gen. AI LLMs, both commercial and open source.
Ensure alignment to appropriate patterns and standards for cloud integration and automation.
Identify opportunities for reuse and improved efficiency.
Engage with IT and Business partners, product owners, and stakeholders to create meaningful roadmaps to ensure the most important work is prioritized.
Champion all Mastercards engineering principles.
Actively participate as a member of the Software Engineering Guild sharing your knowledge, best practices, ideas, and passion for technology.
Help identify and drive meaningful behavior-changing metrics.
All About You
Deep understanding of cloud providers including AWS and Azure, especially:
Experience with AI and GenAI-related cloud services, especially: AWS SageMaker, AWS Bedrock, Azure ML. This should include common commercial Foundational Models (FM) from OpenAI, Anthropic, etc., as well as open-source LLM models deployed in the cloud.
Services, Access controls, Integration and Automation.
GenAI LLM Platform Experience:
Model Evaluation
Model API patterns and implementations
Model Governance
Retrieval Augmented Generation
Orchestration
Prompt Libraries
Agents
Tools/Functions
Prompt/Tuning
Chunking Methods
Vector Stores/DBs/Embeddings
Foundational LLM Models (commercial and open source)
Model Hubs
Fine-Tuned Models
AI-related Data Platforms (i.e., DataBricks, IBM watsonx, or similar).
GenAI Code Assistants and Developer Experience (i.e., GitHub Copilot, AWS Code Whisperer, etc.).
Deep AI/ML experience with data science, data analytics, etc.
Solid understanding of cloud security in highly regulated market segments and countries.
Solid experience with site reliability engineering mindset and creating solutions that are resilient, supportable, and observable at all layers of the stack.
Deep understanding of automation using various tools.
Deep understanding of observability in a cloud environment.
Proficient in web service design, standards, best practices, and implementation.
Deep understanding of containerization and designing ephemeral solutions.
Solid understanding of pure Kubernetes and cloud provider-based managed services Kubernetes.
Proven track record of delivering solutions to complex, multi-domain environments.
Ability to articulate complex designs and solutions to people with varying levels of technical aptitude.
Experienced in guiding less experienced engineers with the use of pair programming, code reviews, design reviews, etc.
Deep knowledge in migration from legacy technologies and mindset to the best-in-class solutions for the cloud.
Self-driven and able to navigate complex organizational environments.
Strong communication skills both written and verbal.
Strong understanding of different project management methodologies including waterfall and Agile/Scrum.
Strong understanding of all phases of the SDLC process from design to deployment.
Enthusiastically engages engineers across Technology organizations to promote standard software patterns and reuse of common libraries and services with experience leading open-source development efforts.
Champions performance engineering practices to ensure that performance meets (or exceeds) expectations; educates stakeholders on performance testing processes, methodology, performance and scalability metrics, capacity modeling techniques, and testing approaches.
Understands software development productivity metrics (e.g., code churn, commit size, commits/story) and helps teams to remove blockers and continuously improve code velocity, quality, and release frequency.
Experienced with Python, Java, and other programming languages.
Corporate Security ResponsibilityAll activities involving access to Mastercard assets, information, and networks come 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;
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
Pay RangesNew York City, New York: $198,000 - $317,000 USDO'Fallon, Missouri: $165,000 - $264,000 USD
#J-18808-Ljbffr
We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a
culture of inclusion
for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.
Title and SummaryGen AI Platform Principal Software Engineer
OverviewThe Mastercard Cloud Team, in Mastercard’s ONE division, owns and drives cloud usage and adoption for the company. The team has a unique opportunity to drive cloud patterns, standards, and best practices across all of Mastercard and their M&A’s. This cloud engineering role is focused on building a holistic platform for Generative AI Large Language Models (LLMs).
Role
Design, configure, and implement a Gen. AI platform, including MLOps/LLMOps for Gen. AI LLMs, both commercial and open source.
Ensure alignment to appropriate patterns and standards for cloud integration and automation.
Identify opportunities for reuse and improved efficiency.
Engage with IT and Business partners, product owners, and stakeholders to create meaningful roadmaps to ensure the most important work is prioritized.
Champion all Mastercards engineering principles.
Actively participate as a member of the Software Engineering Guild sharing your knowledge, best practices, ideas, and passion for technology.
Help identify and drive meaningful behavior-changing metrics.
All About You
Deep understanding of cloud providers including AWS and Azure, especially:
Experience with AI and GenAI-related cloud services, especially: AWS SageMaker, AWS Bedrock, Azure ML. This should include common commercial Foundational Models (FM) from OpenAI, Anthropic, etc., as well as open-source LLM models deployed in the cloud.
Services, Access controls, Integration and Automation.
GenAI LLM Platform Experience:
Model Evaluation
Model API patterns and implementations
Model Governance
Retrieval Augmented Generation
Orchestration
Prompt Libraries
Agents
Tools/Functions
Prompt/Tuning
Chunking Methods
Vector Stores/DBs/Embeddings
Foundational LLM Models (commercial and open source)
Model Hubs
Fine-Tuned Models
AI-related Data Platforms (i.e., DataBricks, IBM watsonx, or similar).
GenAI Code Assistants and Developer Experience (i.e., GitHub Copilot, AWS Code Whisperer, etc.).
Deep AI/ML experience with data science, data analytics, etc.
Solid understanding of cloud security in highly regulated market segments and countries.
Solid experience with site reliability engineering mindset and creating solutions that are resilient, supportable, and observable at all layers of the stack.
Deep understanding of automation using various tools.
Deep understanding of observability in a cloud environment.
Proficient in web service design, standards, best practices, and implementation.
Deep understanding of containerization and designing ephemeral solutions.
Solid understanding of pure Kubernetes and cloud provider-based managed services Kubernetes.
Proven track record of delivering solutions to complex, multi-domain environments.
Ability to articulate complex designs and solutions to people with varying levels of technical aptitude.
Experienced in guiding less experienced engineers with the use of pair programming, code reviews, design reviews, etc.
Deep knowledge in migration from legacy technologies and mindset to the best-in-class solutions for the cloud.
Self-driven and able to navigate complex organizational environments.
Strong communication skills both written and verbal.
Strong understanding of different project management methodologies including waterfall and Agile/Scrum.
Strong understanding of all phases of the SDLC process from design to deployment.
Enthusiastically engages engineers across Technology organizations to promote standard software patterns and reuse of common libraries and services with experience leading open-source development efforts.
Champions performance engineering practices to ensure that performance meets (or exceeds) expectations; educates stakeholders on performance testing processes, methodology, performance and scalability metrics, capacity modeling techniques, and testing approaches.
Understands software development productivity metrics (e.g., code churn, commit size, commits/story) and helps teams to remove blockers and continuously improve code velocity, quality, and release frequency.
Experienced with Python, Java, and other programming languages.
Corporate Security ResponsibilityAll activities involving access to Mastercard assets, information, and networks come 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;
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
Pay RangesNew York City, New York: $198,000 - $317,000 USDO'Fallon, Missouri: $165,000 - $264,000 USD
#J-18808-Ljbffr