Title and Summary

Principal Software Engineer

Role Overview

As a Principal Engineer within the Decision Stream program, you will combine enterprise-scale technical leadership with hands-on engineering for the next-generation Decision Management Platform. This is not a strategy-only role. You will actively design, code, prototype, and validate core platform capabilities, using modern AI-assisted development tools as part of day-to-day software engineering to move faster, improve quality, and help teams adopt better ways of building.

Key areas of focus include leveraging disruptive technologies in real-time AI inferencing and decisioning to improve product effectiveness, increase business delivery, strengthen technical resilience, and lower cost of ownership. You will work closely with technology executives, senior leaders, and engineers to shape the overall AI & DPE technology strategy.

Key Responsibilities

Platform & Product Development
- Build software, tooling, and platform capabilities.
- Design and implement large scale distributed systems.
- Develop reusable services, patterns, and integrations.
- Contribute to new product and prototype development from concept through validation.

Evaluation & Technical Judgment
- Evaluate systems, frameworks, and tools across quality, cost, latency, scalability, reliability , and maintainability.
- Apply sound engineering judgment to trade-offs in distributed systems design and architecture.

Developer Experience & Enablement
- Apply AI tools as part of daily engineering practice to real product and platform problems.
- Teach and model the adoption of AI-assisted development, modern languages, and current engineering practices.
- Improve developer experience through automation, AI-assisted workflows, and platform thinking.
- Advocate learnings, prototypes, and best practices across the organization.

Customer Experience & Platform Strategy
- Own and improve end-to-end customer experience across a portfolio of services and applications.
- Simplify and optimize architecture strategies to balance cost, performance, and business value.
- Apply judgment and experience to make trade-offs between competing priorities and technical constraints.

Thought Leadership & Influence
- Lead architectural design for complex, enterprise-wide initiatives spanning multiple services and programs.
- Drive organization-wide initiatives to advance software engineering craftsmanship and best practices.
- Represent the organization through public speaking, technical blogs, and white papers on emerging technologies.
- Participate in Principle-level architecture reviews and resolve enterprise-wide technical and regulatory challenges.

Talent & Culture
- Mentor engineers at all levels, fostering technical growth and leadership.
- Conduct technical interviews to raise the performance bar and attract top talent.
- Provide unbiased, accomplishment-based recommendations for promotions.
- Champion AI-assisted engineering as a default working practice and align it with organizational values.

What We're Looking For

You are an awesome engineer and leader who is passionate about joining a like-minded team at Mastercard AI & Decision Product Enablement. You thrive on solving complex engineering challenges at scale and have experience building high-speed streaming platforms or distributed systems at hyperscaler-level performance.
• AI-native engineer — uses AI-assisted development as default
• Innovation leader — builds systems at massive scale and availability
• Streaming-first mindset — experience with low-latency pipelines
• Proven outcomes — delivers impactful, production-ready systems
• Engineering culture champion — drives best practices and transparency
• Collaborative — works across engineering and data science teams

Technical Domains

Candidates are not expected to be expert in all areas but should demonstrate hands-on depth and expertise in one or more of the following:

Decisioning Data & Feature Platforms
- Data architectures for decisioning: lakehouses, delta lakes, distributed logs, and product-aligned data models.
- Feature catalogs and engineering platforms for reusable, governed features that can be defined, discovered, validated, and served across batch and real-time decisioning.
- Decisioning data models covering events, derived features, reference data, labels, outcomes, and policy data consumed by rules, models, and agentic workflows.
- Data contracts, lineage, freshness, and quality controls that keep decisioning data trustworthy, explainable, and production ready.

High-Throughput, Low-Latency & Real-Time Systems
- Event streaming and high-throughput data pipelines.
- Low-latency data technologies -distributed caches, in-memory data grids.
- Real-time transaction processing and sub-second decisioning.

AI & ML Systems
- ML lifecycle engineering: model training, deployment, refresh, and low-latency inference.
- Agentic AI patterns, LLM integration, and prompt engineering applied to platform and product problems.
- Model observability, drift detection, and feedback loops that keep AI systems reliable in production.

Decisioning Tooling & UX
- Authoring, testing, and deployment of business rules engine rules and similar decisioning logic.
- Tooling that helps authors validate rules, models, and policies pre-deployment.
- Operator experience for authors, analysts, and SREs: lifecycle, approvals, observability, and explainability of live decisions.
- Rules engine design and modernization.

Cloud Infrastructure, Platform Engineering & DevOps
- AWS infrastructure engineering and cloud-native platform patterns.
- DevOps and platform engineering -automation, CI/CD, observability, GitOps.

Requirements
- Extensive experience in software engineering and technical leadership
- Proven delivery of large-scale distributed systems
- Expertise in cloud, AI/data platforms, and modern engineering practices
- Strong communication and mentorship skills
- Bachelor’s degree (or equivalent); advanced degree preferred
- Telecommuting and/or working from home may be permissible pursuant to company policies.