MastercardSenior AI Engineer
Mastercard is hiring a Senior AI Engineer 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.
- Tools and skills the posting cites include OpenAI, Anthropic and Gemini.
- Level classifies it as a senior role.
- Level lists 1,100+ other open roles at Mastercard.
- The role was posted in the last few days.
AI in this role
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 EngineerMastercard's Business & Market Insights (B&MI) group empowers organizations to achieve growth and innovation goals by delivering unparalleled data-driven intelligence and cutting-edge AI solutions. By harnessing proprietary data, frontier generative AI, and global expertise, B&MI helps businesses make smarter, faster, and more impactful decisions. We transform complex, multi-modal data into agentic systems and generative applications that drive measurable business outcomes and sustained competitive advantage.We're looking for a Senior AI Engineer, Generative AI & ML Engineering, for the Operational Intelligence Program within B&MI. In this hands-on role, you'll design, build, and ship production-grade agentic AI systems. These include agents that make extensive tool calls into enterprise services and APIs, handle complex multi-step workflows, and deliver secure, scalable, reliable results. You'll work across agent orchestration, tool calling, memory, retrieval, and LLMOps, help set the engineering standard for Gen AI development, and mentor other engineers on the team. Much of this work is strong backend engineering. If you bring deep experience building production services and have started building with LLMs, we'd love to
Roles & Responsibilities
• Architect and build the tool-calling layer that powers our agents: wrap internal services, data platforms, and external APIs as well-defined tools with clear schemas, contracts, and permissions.
• Engineer reliable tool execution at scale, including parallel and chained tool calls, timeouts, retries, idempotency, error recovery, and graceful handling of tool failures or bad model outputs.
• Develop high-performance Python backend services for LLM orchestration, covering async job handling, streaming responses, caching, rate limiting, and distributed workflows supporting high-throughput Gen AI operations.
• Engineer state, memory, and context management subsystems so agents can maintain session continuity across long, multi-step, tool-heavy interactions.
• Architect multi-agent and skills-based agent systems using frameworks such as LangGraph, CrewAI, AutoGen, or similar, enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale.
• Build observability for agent behavior, including end-to-end tracing of tool calls, measuring tool-selection accuracy, and debugging multi-step agent runs in production.
• Build retrieval systems (RAG and Graph-RAG) where agents need grounded knowledge, using vector databases (e.g., Pinecone, pgvector, OpenSearch) and knowledge graphs (e.g., Neo4j, AWS Neptune).
• Establish LLMOps and MLOps pipelines on Databricks (AWS), covering MLflow, prompt and agent evaluation, model lineage tracking, CI/CD, and monitoring.
• Design multimodal generative AI pipelines that unify text, image, tabular, and graph data using modern transformer-based and foundation models.
• Guide fine-tuning, prompt engineering, and alignment strategies (e.g., PEFT, LoRA, instruction tuning, RLHF) to adapt foundation models for specialized enterprise use cases, working closely with ML specialists.
• Implement Responsible AI practices, including hallucination mitigation, bias detection, output safety guardrails, explainability, and compliance with data ethics standards.
• Evaluate and productionize advances in agentic AI, multimodal models, and frontier foundation models, benchmarking them against enterprise performance and safety requirements.
• Lead, mentor, and grow a team of engineers, and set the engineering standard for Gen AI development across the program.
All About You
• Bachelor's or Master's degree in Computer Science, AI/ML, or Engineering, or equivalent practical experience.
• A strong track record of building and operating production backend systems at scale, including distributed services, APIs, async processing, data stores, and observability.
• Strong API design and integration experience: you know how to build services that other systems, and now LLMs, can call safely and reliably.
• Excellent Python engineering skills, including async programming, API development (FastAPI or similar), and microservices, plus SQL proficiency.
• Hands-on experience shipping at least one LLM-powered application to production that uses tool/function calling, such as agents that call APIs, query databases, or trigger workflows. This can be with OpenAI, Anthropic, Gemini, or open-source models.
• A working understanding of Gen AI engineering patterns, such as prompt design, embeddings, semantic search, and agent memory, or a demonstrated ability to ramp up quickly on new technical domains.
• Experience with at least one cloud platform (AWS preferred; Azure or GCP welcome).
• Experience leading or mentoring engineers, plus strong communication and stakeholder management skills. You should be able to translate technical work into business value.
• Nice to have: experience with Model Context Protocol (MCP) or building tool servers and connectors for LLM agents.
• Nice to have: experience evaluating agent performance, such as tool-selection accuracy, task completion rates, or multi-step reasoning reliability.
• Nice to have: exposure to fine-tuning (LoRA, PEFT, RLHF), multimodal models, knowledge graphs, or MLOps tooling like MLflow, Weights & Biases, SageMaker, or Bedrock.
• Coming from a backend background? We value engineers who take AI from prototype to production as much as those who train the models. If you've built high-throughput systems and started applying those skills to LLM-powered products, we want to hear from you, even if you haven't worked with every tool listed here
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 at Mastercard rates 99 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
- $184k
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 do you structure and test a prompt to get consistent output from a language model?
- 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?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, AI agents, Fine-tuning, and ML Ops. 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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