Senior Software Engineer

Mastercard Mastercard · Fintech · O Fallon, MO +1 · Engineering

Mastercard is seeking a Senior Software Engineer to join their B&MI Technology Development team. This role focuses on delivering AI-driven capabilities by building and owning Java services and APIs that deliver AI-powered features, and developing/productionizing AI components in Python. The role involves implementing and operating AI systems in production, managing the full lifecycle from design to deployment, and mentoring other engineers. It blends deterministic systems engineering with AI engineering for probabilistic systems.

What you'd actually do

  1. Build and own Java services and APIs that deliver AI-powered features, ensuring performance, scalability, and maintainability in a distributed environment
  2. Develop and productionize AI components in Python, supporting model training, tuning, and inference workflows
  3. Implement and operate AI systems in production, including deployment frameworks, automated pipelines, and model lifecycle management (versioning, monitoring, and updates)
  4. Own delivery across the full lifecycle—design, development, testing, deployment, configuration, and documentation—with a strong focus on automation and operational excellence
  5. Drive engineering excellence through code reviews, testing discipline, and best practices, making pragmatic design and architectural decisions

Skills

Required

  • Python
  • AI Frameworks (PyTorch, TensorFlow, Hugging Face)
  • Java engineering
  • Testing discipline (unit, integration)
  • AI/ML lifecycle (deployment, workflow automation, performance monitoring)
  • Microservices
  • APIs
  • Distributed systems
  • Data systems (relational, non-relational databases, large-scale data processing)
  • Problem-solving and debugging
  • Communication and collaboration

Nice to have

  • AI systems with data ingestion, preprocessing, and feature engineering pipelines
  • CI/CD practices to AI delivery
  • Observability and monitoring for production AI systems
  • Improving AI system robustness through evaluation, monitoring, and operational safeguards
  • Cloud-native and DevOps technologies (Docker, CI/CD pipelines, PCF or other cloud platforms)
  • Payments, analytics, or reporting platforms
  • Emerging AI-assisted development tools

What the JD emphasized

  • Python – Proven experience building and operationalizing production-grade AI components (code, testing, packaging)
  • AI Frameworks – Hands-on experience with modern AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face) for model development, tuning, or serving
  • Strong Java engineering expertise
  • Proven ability to deliver reliable, production-grade software with strong testing discipline
  • Hands-on experience with the AI/ML lifecycle, including deployment, workflow automation, and performance monitoring

Other signals

  • AI-driven capabilities
  • AI-enabled services
  • AI Engineering
  • model lifecycle
  • production-grade AI components