AI Consultant (Principal AI Engineer) at Mastercard

What you’ll do:

• Partner-facing solution consulting: Engage directly with partners and customers to understand their system architectures, integration patterns, and data environments. Lead technical discovery sessions and act as a trusted advisor on applying Agentic AI solutions within their constraints.
• Architect AI-enabled solutions: Design end to end architectures for Agentic AI systems, including agent orchestration, data flows, model integration, APIs, and security boundaries. Ensure designs align with partner environments such as cloud, hybrid, or on prem deployments.
• Translate requirements into blueprints: Convert business and technical requirements into clear solution architectures, reference designs, and implementation guidance that partners can execute against.
• Guide AI and data integration: Advise on data requirements, data readiness, and integration of AI models with enterprise systems. Provide guidance on patterns such as retrieval augmented generation (RAG), tool using agents, and human in the loop workflows.
• Define best practices and guardrails: Apply Responsible AI principles, including data governance, security, safety controls, and risk mitigation. Contribute to standards, templates, and reference architectures for repeatable partner deployments.
• Collaborate with internal teams: Work with product, engineering, and platform teams to align partner needs with product capabilities and roadmap. Support pilots, proofs of concept, and early customer implementations.
• Technical communication and enablement: Produce architecture diagrams, documentation, and presentations. Clearly explain technical trade offs and architectural decisions to both technical and non technical stakeholders.
• Stay current on Agentic AI: Track emerging tools, frameworks, and architectural patterns in generative and Agentic AI, and guide partners on practical adoption.

What you’ll bring:

• Extensive architecture and engineering experience: 12+ years designing and building complex software systems, with strong depth in system and solution architecture.
• Enterprise solution architecture expertise: Proven experience translating business and technical requirements into scalable architectures involving multiple systems, integrations, and data sources.
• AI and generative AI familiarity: Solid understanding of AI and ML concepts, with hands on exposure to generative AI and LLM based systems in enterprise contexts.
• Agentic AI understanding: Familiarity with agent based architectures, orchestration patterns, and enterprise considerations such as guardrails, observability, and control.
• Partner and consulting mindset: Experience working directly with customers or partners in a consulting, advisory, or solution engineering role. Comfortable influencing architectural decisions.
• Strong communication skills: Ability to explain complex technical concepts clearly, create effective documentation, and engage senior technical and business stakeholders.
• Technical leadership: Experience guiding engineering teams through design decisions, reviews, and implementation challenges.

Required skills:

• Education and fundamentals: Bachelor’s or Master’s degree in Computer Science or a related field, with strong computer science and systems design fundamentals.
• Software engineering proficiency: Strong programming skills in languages such as Python, Java, or JavaScript/TypeScript. Ability to reason across backend systems, APIs, and data layers.
• Systems and cloud architecture: Experience designing distributed systems on AWS, Azure, or GCP. Familiar with microservices, event driven architectures, and API centric design.
• AI and data integration: Working knowledge of AI solution lifecycles, including data preparation, model integration, embeddings, vector databases, and prompt based systems.
• Security and governance awareness: Understanding of enterprise security, data privacy, and Responsible AI considerations.
• Analytical problem solving: Ability to evaluate architectural options, assess trade offs, and recommend pragmatic solutions for partner environments.
• Collaboration and delivery: Experience working in agile environments, collaborating across teams, and supporting solutions from design through early delivery.