MastercardSenior AI Engineer, AI Engineering
Mastercard is hiring a Senior AI Engineer, AI Engineering in Pune, India. Level rates it ; you can apply on Mastercard.
Level's read on this job
- Level rates this AI engineer role Builds AI ●●●●: the job is building AI systems.
- The posting mentions LangGraph, CrewAI and AutoGen among its tools and skills.
- Level classes it as a senior role.
- Mastercard has 967 other open roles on Level.
AI in this role
Senior AI engineer leading machine learning development for multi-agent intelligence frameworks, RAG systems, and transformer models.
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Senior AI Engineer, AI EngineeringMastercard’s Business & Market Insights (B&MI) group empowers organizations to achieve growth & innovation goals by providing unparalleled data-driven insights and advanced analytics. By leveraging proprietary data and global expertise, B&MI helps businesses make smarter, more informed decisions that drive profitability and success. We turn complex data into actionable strategies that lead to better outcomes and sustained competitive advantage.We are currently looking for a ‘Lead Engineer, Machine Learning Engineering’ for Operational Intelligence Program, within B&MI group. This role will lead ML engineering team to execute on AI/ML strategy for the program that enables business growth, enhances customer experience, and ensures delivery of secure, scalable, and high-performing software solutions. As a technology leader, this role will also focus on engineering best practices, next gen innovation and stakeholder management, while fostering a culture of continuous learning and technical excellence within the team.
Roles and Responsibilities:
• Implement multi-agent intelligence frameworks (LangGraph, CrewAI, AutoGen) to enable reasoning, coordination, and adaptive decision-making across specialized AI agents.
• Design and operationalize multi-modal AI pipelines combining text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, etc.) for unified intelligence.
• Build scalable RAG and Graph-RAG systems integrating vector stores and knowledge graphs (Neo4j, AWS Neptune) to enable contextual retrieval, semantic linking, and entity-aware reasoning.
• Develop and productionize transformer-based models for NLP, vision-language understanding, and sequential prediction tasks leveraging Hugging Face, PyTorch, and TensorFlow ecosystems.
• Implement advanced Python-based backend services for inference orchestration, async job handling, and distributed data workflows supporting high-throughput AI operations.
• Establish end-to-end LLMOps and MLOps pipelines on Databricks (AWS) integrating MLflow, feature stores, model lineage, prompt evaluation, and continuous retraining frameworks.
• Apply traditional AI/ML and statistical modeling techniques (regression, clustering, forecasting, ensemble methods) alongside deep learning models for hybrid interpretability and explainability.
• Engineer state and memory management subsystems that preserve context, track embeddings, and enable agents to reason temporally across multiple modalities and interactions.
• Implement Responsible AI practices—bias detection, explainability dashboards, data ethics checks, and performance governance ensuring fairness and transparency of deployed models.
• Continuously research, benchmark, and productionize innovations in multimodal transformers, generative modeling, and agentic orchestration to drive enterprise-scale intelligence and automation.
All About You:
• Master’s/bachelor’s degree in computer science or engineering, and a considerable work experience with a proven track-record of successfully leading and managing complex projects/products and delivering to aggressive market needs.
• Expert-level hands on experience designing, building and deploying both conventional AI/ML solutions and LLM/Agentic solutions.
• Strong analytical and problem-solving abilities, with quick adaptation to new technologies, methodologies, and systems.
• Strong applied knowledge and hands on experience in advanced statistical techniques, predictive modelling, machine learning algorithms, GenAI and deep learning frameworks. Experience with AI and machine learning platforms such as TensorFlow, PyTorch, or similar.
• Strong programming skills in languages such as Python/SQL is a must. Experience with data visualization tools (e.g., Tableau, Power BI) and understanding of cloud computing services (AWS, Azure, GCP) related to data processing and storage is a plus.
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.
How we rate this
Senior AI Engineer, AI Engineering at Mastercard rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI engineer jobs at this level
Pay
- Typical AI engineer job
- $183k
This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- How do you think about the risk of an AI system in this kind of role failing silently?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, ML Ops, NLP, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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