Mastercard Off-Campus Recruitment 2026: Hiring AI Engineer in Gurgaon

If you are looking to build a career in cutting-edge Artificial Intelligence and Generative AI within a top-tier global financial technology organization, this opening from Mastercard is worth your attention. Mastercard is currently inviting applications for the position of AI Engineer at their Gurgaon technology center. This position sits right inside the Global Pricing & Interchange (P&IC) unit, where data-driven strategies and automated decision systems directly impact Mastercard's revenue models across worldwide markets.

In this position, you will not just be building basic machine learning scripts. Instead, you will be expected to architect production-ready Large Language Model (LLM) workflows, implement Retrieval-Augmented Generation (RAG) pipelines, build multi-agent systems, and deploy cloud-native AI tools that automate real-world business decisions. Below, you will find all the crucial details including job duties, eligibility guidelines, technical skill requirements, hiring process, and direct application links.

About Mastercard

Mastercard is a recognized leader in payments and financial technology, driving digital transaction infrastructure in over 200 countries and territories. The company serves consumers, businesses, merchants, banks, and governments by making digital transactions simple, smart, secure, and accessible. Beyond standard payment cards, Mastercard operates advanced technology networks that handle fraud prevention, cyber security, open banking, and complex financial strategy processing.

Working at Mastercard gives technology professionals exposure to enterprise systems operating at enormous global scale. The company places heavy emphasis on continuous learning, career development, and modern engineering methodologies. Engineers at Mastercard work with modern cloud infrastructure, advanced data systems, and generative AI frameworks to build services that power global commerce.

Role Overview & Key Responsibilities

As an AI Engineer in the Global Pricing & Interchange team, your main responsibility will be designing, constructing, and managing enterprise AI applications. You will turn raw financial ideas and pricing workflows into working AI applications that reduce manual effort and improve strategic decision-making.

Here is a clear look at what your day-to-day work will involve:

• Developing GenAI Applications: Create and maintain enterprise LLM workflows, RAG pipelines, and agentic solutions from early prototype stage all the way to production deployment.
• Agentic System Architecture: Design multi-agent workflows, inter-agent orchestration systems, and custom tools to automate complex pricing and business analytics tasks.
• Backend Integration: Write solid Python backend microservices, REST APIs, and software layers needed to connect AI models with enterprise data sources.
• Cloud Data Pipelines: Build and maintain cloud data workflows on Azure or AWS using platforms like Databricks, Spark, and vector databases.
• MLOps & LLMOps Practices: Set up automated CI/CD pipelines, experiment tracking, model monitoring, continuous evaluation, and versioning for AI models in production.
• Responsible AI & Safety: Apply security and privacy controls, set up hallucination checks, bias monitoring, explainability frameworks, and safety guardrails.
• Cross-Functional Collaboration: Work directly with product owners, business analysts, data scientists, and senior engineering leaders to turn requirements into effective tech solutions.

Eligibility Criteria & Educational Qualifications

To be considered for this position, applicants should meet the following requirements:

• Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical discipline.
• Practical Experience: Prior experience in designing, building, and deploying real-world machine learning, deep learning, or Generative AI systems into production.
• Fintech Domain Advantage: Previous work experience in payments, banking, financial services, pricing, or enterprise business analytics is helpful, though not mandatory.

Required Skills & Preferred Technical Knowledge

Mastercard is looking for candidates with strong hands-on coding ability and practical experience in Generative AI ecosystems. The primary technical areas include:

• Programming & Frameworks: Fluent in Python, along with API frameworks (FastAPI/Flask) and microservices architecture.
• Generative AI Stack: Hands-on familiarity with OpenAI, Azure OpenAI, LangChain, LangGraph, Hugging Face, Gemini, and open-source models (LLaMA, Mistral).
• Core Concepts: Clear understanding of prompt engineering techniques, vector embeddings, vector databases (Chroma, Pinecone, Qdrant), semantic search, and agent orchestration.
• Cloud & Big Data: Experience using Azure or AWS, combined with big data platforms such as Databricks, Apache Spark, and standard SQL databases.
• MLOps & Lifecycle: Knowledge of model deployment tools, CI/CD pipelines, logging, tracing, and model evaluation frameworks.

Selection & Interview Process

The selection workflow at Mastercard generally involves multiple evaluation stages to assess technical competency, problem-solving skills, and culture alignment:

1. Online Profile Screening: The talent acquisition team checks resumes based on academic background, technical skills, and past project experience in AI/ML.
2. Technical Screening Round: A initial technical discussion or online coding assessment focusing on Python programming, data structures, and foundational AI concepts.
3. Deep Dive Technical Interviews (2 Rounds): In-depth discussion covering system design for AI applications, RAG architecture, LLM fine-tuning/prompting, MLOps, vector database design, and real-world project scenarios.
4. Managerial & Behavioral Round: Discussion around previous project impact, teamwork skills, communication, problem solving, and alignment with Mastercard values.

Preparation Tips for Candidates

• Highlight End-to-End Projects: Make sure your resume clearly mentions AI or LLM projects that went beyond basic tutorials into actual deployment or functional web applications.
• Master RAG Architecture: Be ready to explain chunking strategies, embedding models, vector indexing, retrieval accuracy tuning, and evaluation frameworks.
• Revise Python & SQL: Strong fundamentals in Python and data query optimization are frequently tested during coding assessments.

How to Apply

Interested candidates can apply directly through the official Mastercard careers page by following these simple steps:

1. Click on the Apply Now button provided below to navigate to the official job posting.
2. Read through the official job description carefully.
3. Click on the Apply button on the Mastercard portal and create or log in to your candidate account.
4. Upload an updated resume highlighting your Generative AI, Python, and cloud engineering projects.
5. Fill in the required personal and education details, then submit your application.