AI Consultant, also known as a Principal AI Engineer, at Mastercard.

Education and fundamentals: You need a Bachelors or Masters degree in Computer Science or a related field with computer science and systems design fundamentals.

Software engineering proficiency: You need programming skills in languages like Python, Java or JavaScript/TypeScript and the ability to reason across backend systems, APIs and data layers.

Systems and cloud architecture: You need experience designing distributed systems on cloud platforms like AWS, Azure or GCP. Be familiar with microservices, event-driven architectures and API-centric design.

AI and data integration: You need a working knowledge of AI solution lifecycles, including data preparation, model integration, embeddings, vector databases and prompt-based systems.

Security and governance awareness: You need an understanding of enterprise security, data privacy and Responsible AI considerations.

Analytical problem solving: You need the ability to evaluate options assess trade-offs and recommend pragmatic solutions for partner environments.

Collaboration and delivery: You need experience working in environments collaborating across teams and supporting solutions, from design through early delivery.

Partner-facing solution consulting: I work directly with our partners to understand how their systems work how they integrate with other systems and what kind of data they have. I lead meetings to figure out how to use AI solutions in a way that works for them. I am like a trusted advisor to them.

Architect AI-enabled solutions: I design systems for AI, including how the different parts work together how data moves how models are integrated and how to keep everything secure. I make sure these designs work with our partners systems whether they are in the cloud on their servers or a mix of both.

Translate requirements into blueprints: I take what our partners need and turn it into a plan for how to build the solution. This plan includes diagrams, instructions and examples that our partners can follow.

Guide AI and data integration: I help our partners figure out what data they need, how to get it ready and how to integrate AI models with their systems. I also give them advice on how to use patterns, like retrieval augmented generation and how to work with AI tools.

Define practices and guardrails: I make sure we are using AI in a responsible way by following rules and guidelines for data governance, security, safety and risk. I help create standards and templates that our partners can use.

Collaborate with teams: I work with our product, engineering and platform teams to make sure our partners needs are met. I help with testing and early implementations.

Technical. Enablement: I create diagrams, documents and presentations to explain technical ideas in a simple way. I can talk to both non-technical people.

Stay current on Agentic AI: I keep up to date with the tools, frameworks and patterns in AI and I help our partners figure out how to use them.

To be good at this job you need:

architecture and engineering experience: You should have twelve or more years of experience designing and building complex software systems with a strong understanding of system and solution architecture.

Enterprise solution architecture expertise: You should have experience turning business and technical requirements into architectures that involve many systems, integrations and data sources.

AI and generative AI familiarity: You should have an understanding of AI and machine learning concepts and hands-on experience with generative AI and large language models in enterprise contexts.

Agentic AI understanding: You should be familiar with agent-based architectures orchestration patterns and enterprise considerations like guardrails, observability and control.

Partner and consulting mindset: You should have experience working directly with customers or partners in a consulting or advisory role and be comfortable influencing decisions.

Communication skills: You should be able to explain complex technical concepts clearly create effective documentation and engage with senior technical and business stakeholders.

Leadership: You should have experience guiding engineering teams through design decisions, reviews and implementation challenges.