AI Engineer II
Mastercard · Posted Oct 26
Global technology company connecting consumers, financial institutions, merchants, and businesses through secure payment processing and digital commerce solutions
Get a personal compatibility score
Add a resume for personal matches
About the role
Mastercard's Business & Market Insights (B&MI) group delivers data-driven intelligence and AI solutions using proprietary data, generative AI, and global expertise. The Operational Intelligence Program builds production-grade agentic AI systems, generative applications, and multimodal pipelines. This role designs and ships tool-calling agents, Python backend services, retrieval systems, and LLMOps/MLOps on Databricks/AWS, and mentors engineers.
What you will do
- 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.
Skills used in this role
What the employer is looking for
- 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.
Preferred qualifications
- 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.
Benefits and support
- Compensation and benefits are detailed in the job posting
About Mastercard
Mastercard is a global technology company in the payments industry that connects consumers, financial institutions, merchants, governments, and businesses worldwide. It operates a fast and secure payment processing network that spans more than 200 countries and territories. Through innovative products and solutions in credit, debit, digital assets, and security, Mastercard makes commerce safer, simpler, and more accessible.
- Industry
- Payments
- Company size
- 10001+ employees
- Founded
- 1966
- Location
- Purchase, New York, USA
- Funding stage
- Public Company
Funding
Public Company · $115M raised
Leadership
Chief Executive Officer
Chief Business Officer
President & Chief Technology Officer
Chief People Officer
Recent coverage
Mastercard Newsroom
Mastercard introduces risk intelligence to enable more secure stablecoin payments2026-10-19
Mastercard Newsroom
Mastercard Max makes benefits easy to discover, understand and use2026-10-21
Mastercard Newsroom
Mastercard advances agentic commerce with new trust and intelligence services2026-09-15