Description
Mastercard Payment Networks Technology team is looking for a Sr. Software Engineer to drive our customer experience strategy forward by consistently innovating and problem-solving.
The ideal candidate is passionate about the customer experience journey, highly motivated, intellectually curious, analytical, and possesses an entrepreneurial mindset.
Responsibilities:
- Independently translate business and system requirements into pragmatic designs, modular code, rapid AI proofs of concept (PoCs), minimum viable products (MVPs), and production-ready applications.
- Build AI-powered applications and backend services using hosted foundation-model APIs, SDKs, enterprise data, and reusable services; evaluate ideas quickly and recommend whether they should progress, pivot, or stop.
- Build end-to-end Retrieval-Augmented Generation (RAG) solutions covering ingestion, chunking, embeddings, vector or hybrid retrieval, reranking, grounding, citations, and permission-aware responses.
- Build and integrate Model Context Protocol (MCP) clients and servers that expose enterprise tools, prompts, resources, data, and services through standardized, governed, and auditable interfaces.
- Develop agentic workflows using tool calling, orchestration, state and memory patterns, structured outputs, guardrails, confidence controls, output validation, and human-in-the-loop approvals.
- Use Claude, GitHub Copilot, and comparable AI-assisted engineering tools hands-on to accelerate discovery, prototyping, coding, testing, documentation, and code review while protecting confidential information and validating generated output.
- Define engineering standards, evaluation criteria, and automated validation processes for AI-powered applications and developer productivity solutions, ensuring high quality, reliability, security, maintainability, and operational excellence.
- Productionize validated AI solutions using secure coding, automated testing, CI/CD, configuration management, observability, resilience, performance optimization, and rollback strategies.
- Embed security, privacy, compliance, Responsible AI, least-privilege access, role-based authorization, secrets management, data minimization, content safety, and auditability into solution design.
- Collaborate with product owners, architects, platform engineers, quality engineers, domain experts, and business stakeholders to resolve roadblocks and deliver high-quality outcomes in a fast-paced Agile environment.
Requirements:
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, or a related field, or equivalent practical experience.
- Strong software engineering experience delivering secure, reliable enterprise applications, APIs, backend services, or distributed systems, with the independence expected of a Senior Software Engineer.
- Hands-on experience building AI-enabled applications using modern programming languages, enterprise data, APIs, and AI-assisted development tools.
- Practical experience with LLM applications, RAG, agentic workflows, or similar applied AI patterns.
- Hands-on experience using AI application frameworks, hosted model services, and relevant SDKs to build and integrate enterprise AI solutions.
- Strong understanding of API design, distributed systems, automated testing, secure development, CI/CD, and production support practices.
Preferred qualifications:
- Experience in payments, high-volume transaction processing, or other highly available and regulated technology environments.
- Familiarity with cloud-native platforms, observability, resiliency, and Responsible AI practices.
Skills, Tools & Technologies:
- Core: Python or another modern programming language, experience with SQL and other relational or NoSQL databases, backend services and distributed systems for integrating enterprise data, AI capabilities, and resilient high-volume transaction workflows, strong debugging skills, Git, and automated testing.
- AI workflows and quality: LLM application development and RAG, with emphasis on prompts and agent workflows, and guardrails and evaluation logic.
- AI platforms and frameworks: Knowledge of Anthropic APIs, AWS Bedrock, Azure OpenAI, or equivalent hosted-model services, and LangChain, LangGraph, or equivalent SDKs.