Lead Software Engineer, Backend (Java/Python & AI/LLM)

Overview

Lead Software Engineer, Backend (Java/Python & AI/LLM)

Lead Software Engineer, Backend – Foundry R&D, Pune

We are looking for a Lead Software Engineer to join the Mastercard Foundry R&D team.You will help build scalable systems that support our AI innovation efforts. The ideal candidate is hands-on, curious, motivated, and comfortable working in fast-moving R&D environments.

What you'll do

• Lead backend services for AI features: Build and maintain backend components and APIs for generative AI use cases. Create scalable microservices in Java or Python to expose AI capabilities, handle requests, and integrate model outputs into applications.
• Integrate generative AI technologies: Work with data science and ML teams to productionize models and connect them to the platform. Build service interfaces, manage data formats, integrate external APIs, and implement supporting data flows such as caching or context retrieval.
• Ensure quality, performance, and security: Own service quality by writing tests, profiling performance, and resolving bottlenecks. Set up monitoring and alerts, improve logging, and diagnose production issues to ensure uptime and stability. Maintain code quality through reviews and enforce security and performance best practices.
• Collaborate cross‑functionally: Work in an agile team with product, design, and data science. Participate in design discussions, refine requirements, and iterate quickly based on feedback. Help shape technical decisions for AI‑powered features.
• Mentor and uphold best practices: Guide junior engineers through code reviews and knowledge sharing. Promote clean coding, maintainability, testing discipline, and improvements to tools and processes.
  
  
  

What you'll bring

• Strong backend engineering experience: 8–12+ years building backend systems and APIs. Experience with Java (Spring Boot) or Python and familiarity with scalable, thread‑safe server‑side development.
• Experience with AI/ML integration: Hands‑on exposure to AI APIs or machine learning services. Comfortable integrating pre‑trained models, calling inference APIs, handling asynchronous tasks, and building reliable ML service endpoints.
• API and database proficiency: Skilled in designing RESTful APIs, managing authentication, and structuring data. Strong SQL knowledge and experience with relational and NoSQL databases, caching, and data‑intensive flows.
• Quality‑focused and detail‑oriented: Strong testing habits, including unit and integration tests. Familiar with error handling, logging, edge cases, and building resilient AI‑related services.
• Architectural leadership: Background designing modular, scalable backend systems. Experience promoting clean structure, reusable components, and documentation.
• Strong communication and collaboration: Able to work with distributed teams and communicate clearly with technical and non-technical stakeholders.
• Continuous learning: Comfortable working with ambiguity and exploring new technologies. Motivated to build prototypes and evolve solutions quickly.
  
  
  

• Education and experience: Bachelor's degree in Computer Science or related field. 8–12+ years of backend or full‑stack engineering in agile teams, with experience delivering complex products.
• Back‑end programming mastery: Expertise in any of Java, Python, or Go, and related frameworks such as Spring Boot or FastAPI. Strong Git workflows, scripting skills, and understanding of concurrency or async development.
• Web services and microservices: Experience building and consuming REST services and working in microservice architectures. Familiar with message queues, API gateways, and tools like Swagger or Postman.
• Database and data management: Strong SQL and schema design skills, use of indexes, query optimization, and ORM familiarity. Experience with NoSQL or caching technologies for performance‑heavy applications.
• Cloud and CI/CD: Experience deploying services on AWS, GCP, or Azure, using containers, serverless or orchestration tools, and CI/CD pipelines to automate builds, tests, and deployments.
• AI services and frameworks (basic exposure): Familiarity with ML concepts, AI APIs, SDKs like OpenAI, or libraries such as TensorFlow/PyTorch for inference. Comfortable working with JSON, embeddings, or common AI data formats.
• Testing and monitoring: Experience writing comprehensive test suites, mocking external services, and using monitoring or APM tools to track service health and performance.
• Agile and teamwork: Experience in agile workflows, breaking down stories, estimating tasks, using tools like JIRA, and communicating clearly across distributed teams.
  
  
  

• Generative AI familiarity: Experience with LLMs, prompt engineering, transformers, or vector databases.
• Performance optimization: Background in improving API latency, scaling systems, using multi‑threading/async, or tuning database and service performance.
• DevOps and automation: Familiarity with Terraform, Kubernetes, IaC, or advanced CI/CD. Ability to contribute to deployment or automation strategies.
• Full‑stack exposure: Understanding of frontend frameworks or mobile app integration to improve end‑to‑end system design.
• Domain knowledge: Interest or experience in payments, finance, or commerce to better understand use cases for AI features.
• Continuous learning and initiative: Certifications, personal projects, open‑source contributions, or evidence of self‑driven learning.
• Achievements and leadership: Experience leading technical initiatives, mentoring peers, or owning critical system components.
  
  
  

#AI3

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;
• Ensure the confidentiality and integrity of the information being accessed;
• Report any suspected information security violation or breach, and
• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.