Gen AI Platform Principal Software Engineer
Job Title:
Gen AI Platform Principal Software Engineer
Overview:
Overview*
The Mastercard Cloud Team, in Mastercards 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&As. The cloud team has a unique opportunity to be at the forefront of an evolving landscape of technology at Mastercard and help shape the future of development, deployment and more!
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 parters, 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:
o 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.
o Services, Access controls, Integration and Automation .etc.
GenAI LLM Platform Experience:
o Model Evaluation
o Model API patterns and implementations
o Model Governance
o Retrieval Augmented Generation
o Orchestration
o Prompt Libraries
o Agents
o Tools/Functions
o Prompt/Tuning
o Chunking Methods
o Vector Stores/DBs/Embeddings
o Foundational LLM Models (commercial and open source)
o Model Hubs
o 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 understandig 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 ephemiral 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 experienceed 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 help teams to remove blockers and continuously improve code velocity, quality, and release frequency
Experienced with Python, Java, and other programming languages
#AI
Apply Now
Gen AI Platform Principal Software Engineer
Overview:
Overview*
The Mastercard Cloud Team, in Mastercards 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&As. The cloud team has a unique opportunity to be at the forefront of an evolving landscape of technology at Mastercard and help shape the future of development, deployment and more!
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 parters, 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:
o 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.
o Services, Access controls, Integration and Automation .etc.
GenAI LLM Platform Experience:
o Model Evaluation
o Model API patterns and implementations
o Model Governance
o Retrieval Augmented Generation
o Orchestration
o Prompt Libraries
o Agents
o Tools/Functions
o Prompt/Tuning
o Chunking Methods
o Vector Stores/DBs/Embeddings
o Foundational LLM Models (commercial and open source)
o Model Hubs
o 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 understandig 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 ephemiral 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 experienceed 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 help teams to remove blockers and continuously improve code velocity, quality, and release frequency
Experienced with Python, Java, and other programming languages
#AI
Apply Now