Senior Software Engineer - Backend​/Platform Agentic AI

Title And Summary

Senior Software Engineer - Backend/Platform Agentic AI

Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible.

The Portfolio Intelligence (PI) program within Mastercard's Business & Market Insights (B&MI) division delivers analytics products that help financial institutions understand and grow their card portfolios. We are building a first‑party AI platform that brings agentic, conversational, and generative AI capabilities directly into our products; powering features like natural‑language analytics, automated report summaries, and personalized dashboard experiences for thousands of customers worldwide.

• Lead end‑to‑end development of agentic AI systems from design through production, including orchestration, tool calling, context engineering, retrieval, and streaming responses.
• Define technical direction for AI capabilities within the PI platform, driving architecture, design patterns, and integration strategies.
• Build and operate AI‑enabled services in Java and Python within a multi‑tenant, customer‑facing environment, ensuring scalability, reliability, and strict data isolation.
• Design and implement production‑grade AI infrastructure, including prompt management, evaluation frameworks, guardrails, observability, and cost/token telemetry.
• Partner with platform teams, vendors, and product teams to deliver integrated, end‑to‑end solutions.
• Establish and enforce engineering standards for AI development—code quality, testing, deployment, and operational readiness.
• Provide hands‑on technical leadership through design reviews, code reviews, pairing, and mentorship.
• Ensure AI solutions meet Mastercard governance, security, and Responsible AI standards in a regulated environment.
• Drive continuous improvement by defining and tracking metrics such as task success rate, latency, cost per interaction, and human intervention rate.

• Proven experience product ionizing AI/ML systems, delivering reliable, scalable services used in real‑world environments.
• Strong engineering expertise in Java (Spring Boot, microservices) and Python (AI/ML tooling, scripting, services).
• Deep experience building agentic or LLM‑based systems: tool/function calling, RAG, context management, prompt engineering, and orchestration.
• Demonstrated technical leadership through design reviews, mentoring, and raising engineering standards without formal people management.
• Strong operational ownership mindset, including observability, incident response, and service reliability.
• Comfortable operating in ambiguity and making pragmatic architectural decisions.
• Clear communicator able to translate complex technical concepts, present tradeoffs, and produce actionable design documentation.
• Effective collaborator across teams, vendors, and distributed organizations.

• Expertise with Java for backend services (Spring Boot, microservices).
• Fluent in Python for AI/ML development (agentic frameworks, scripting, integrations).
• Hands‑on experience building LLM‑powered production systems (API integration, prompt management, streaming, error handling, cost management).
• Experience with agentic frameworks (Lang Graph, Lang Chain, or similar), RAG pipelines, or AI orchestration systems.
• Proven ability to design scalable distributed systems with strong observability (logging, metrics, tracing, alerting).
• Experience with CI/CD and modern SDLC practices (automated testing, quality gates, deployment automation).
• Cloud experience (AWS or Azure), including managed AI/ML services.
• Technical leadership in design reviews, mentoring, and setting engineering standards.
• Solid backend/software engineering experience with ownership of distributed systems in production.

• Experience with multi‑tenant architectures and customer data isolation.
• Familiarity with AI evaluation frameworks (agent evaluation, prompt regression testing, output quality metrics).
• Experience with Databricks, Snowflake, or similar data…