Mastercard
AI Engineering Manager
Join to apply for the AI Engineering Manager role at Mastercard. This role leads a team building production‑grade agentic AI and traditional AI/ML solutions across Mastercard’s business. You own delivery end‑to‑end—from architecture and experimentation to secure, compliant, scalable deployment—partnering closely with business, product, design, security, risk, and platform teams. This hands‑on leadership role combines technical depth, people leadership, and delivery to accelerate AI impact while upholding production‑grade standards for reliability, privacy, and responsible AI. The Role
Hire, develop, and retain a high‑performing team of AI engineers with clear growth paths and inclusive practices. Establish engineering rituals and uphold high bars for code quality, testing, security, and documentation. Define technical strategy and reference architectures for agentic AI solutions and traditional AI/ML solutions. Guide teams from proof‑of‑concept to production—requirements, solution design, backlog, sprint execution, integration, performance, and operational readiness. Drive platform thinking—build reusable agentic AI services, SDKs, and patterns for retrieval, orchestration, guardrails, evaluation, and observability. Lead design and build of agentic AI solutions for priority business workflows across all Mastercard’s business. Implement RAG, function/tool calling, knowledge graph integrations, and domain adapters for enterprise contexts. Stand up evaluation frameworks for quality, safety, latency, and task success—champion prompt and policy versioning. Own CI/CD for models and prompts, feature stores, vector indices, and model/prompt registries. Ensure observability, content safety, and guardrails in production. Partner with data engineering pipelines, legal, and AI governance teams for data contracts and high‑quality datasets. Embed privacy by design, data minimization, and financial services grade security into architectures. Collaborate with risk, compliance, and legal to meet obligations, operationalize responsible AI, and establish model risk management processes. Partner with product managers to define outcomes, prioritize roadmaps, and validate user value through experimentation. Translate complex technical tradeoffs to non‑technical stakeholders, influence investment decisions with clear ROI and risk framing. Drive enablement for internal customers and ensure measurable adoption. Plan for multi‑region, high availability deployments with disaster recovery, performance tuning, and cost optimization. Core Competencies
People leadership—build inclusive, high trust teams; coach engineers; set clear goals; recognize excellence. Delivery excellence—plan, sequence, and execute complex programs, remove roadblocks, deliver reliably. Technical judgment—weigh build/buy, performance vs. cost, guardrails vs. UX, anticipate risks early. Customer obsession—anchor solutions in measurable user and business outcomes. Communication—explain complex concepts simply, adapt message to executives and engineers. Adaptability—learn fast, iterate, scale what works, comfortable with ambiguity and emerging tech. All About You
Bachelor’s or Master’s in Computer Science, Data Science, or related field (or equivalent practical experience). Highly experienced background in software/AI engineering, managing teams delivering production AI/ML or agentic AI systems. Proven track record shipping enterprise‑grade AI solutions at scale—high availability, low latency, strong security, and compliance. Languages and frameworks: Python, PyTorch/TensorFlow, modern microservices. GenAI/LLMs: prompt engineering, RAG, function/tool calling, agent frameworks, vector databases, embeddings. Data & platforms: modern data stacks, event‑driven designs, experience with major clouds and GPU accelerator workflows. MLOps/LLMOps: CI/CD, model & prompt registries, feature stores, model serving, canary/AB, offline/online evals, observability, cost management. Security & responsible AI: secrets management, IAM, network isolation, policy enforcement; familiarity with content safety tooling and responsible AI practices. UX collaboration: partner with design and research on human‑centered, accessible interfaces for AI‑infused workflows. Experience in payments/financial services or regulated environments highly preferred. Knowledge of PCI DSS, GDPR, ISO 42001, and model risk practices; prior work with sensitive data controls. Multi‑region, active deployments, SLA/SLO design, and incident response leadership. Vendor/platform evaluation and contract oversight for AI tooling and foundation models. Publications, patents, OSS contributions in ML/LLM/agents are a plus. Salary
San Francisco, California: $166,000 – $265,000 USD Remote – New York: $138,000 – $221,000 USD EEO Statement
Mastercard is a merit‑based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disability, veteran status, or any characteristic protected by law. If you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com. The Reasonable Accommodations team will respond promptly. All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organization. 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 confidentiality and integrity, report any suspected violation, and complete all periodic mandatory security trainings in accordance with Mastercard guidelines.
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Join to apply for the AI Engineering Manager role at Mastercard. This role leads a team building production‑grade agentic AI and traditional AI/ML solutions across Mastercard’s business. You own delivery end‑to‑end—from architecture and experimentation to secure, compliant, scalable deployment—partnering closely with business, product, design, security, risk, and platform teams. This hands‑on leadership role combines technical depth, people leadership, and delivery to accelerate AI impact while upholding production‑grade standards for reliability, privacy, and responsible AI. The Role
Hire, develop, and retain a high‑performing team of AI engineers with clear growth paths and inclusive practices. Establish engineering rituals and uphold high bars for code quality, testing, security, and documentation. Define technical strategy and reference architectures for agentic AI solutions and traditional AI/ML solutions. Guide teams from proof‑of‑concept to production—requirements, solution design, backlog, sprint execution, integration, performance, and operational readiness. Drive platform thinking—build reusable agentic AI services, SDKs, and patterns for retrieval, orchestration, guardrails, evaluation, and observability. Lead design and build of agentic AI solutions for priority business workflows across all Mastercard’s business. Implement RAG, function/tool calling, knowledge graph integrations, and domain adapters for enterprise contexts. Stand up evaluation frameworks for quality, safety, latency, and task success—champion prompt and policy versioning. Own CI/CD for models and prompts, feature stores, vector indices, and model/prompt registries. Ensure observability, content safety, and guardrails in production. Partner with data engineering pipelines, legal, and AI governance teams for data contracts and high‑quality datasets. Embed privacy by design, data minimization, and financial services grade security into architectures. Collaborate with risk, compliance, and legal to meet obligations, operationalize responsible AI, and establish model risk management processes. Partner with product managers to define outcomes, prioritize roadmaps, and validate user value through experimentation. Translate complex technical tradeoffs to non‑technical stakeholders, influence investment decisions with clear ROI and risk framing. Drive enablement for internal customers and ensure measurable adoption. Plan for multi‑region, high availability deployments with disaster recovery, performance tuning, and cost optimization. Core Competencies
People leadership—build inclusive, high trust teams; coach engineers; set clear goals; recognize excellence. Delivery excellence—plan, sequence, and execute complex programs, remove roadblocks, deliver reliably. Technical judgment—weigh build/buy, performance vs. cost, guardrails vs. UX, anticipate risks early. Customer obsession—anchor solutions in measurable user and business outcomes. Communication—explain complex concepts simply, adapt message to executives and engineers. Adaptability—learn fast, iterate, scale what works, comfortable with ambiguity and emerging tech. All About You
Bachelor’s or Master’s in Computer Science, Data Science, or related field (or equivalent practical experience). Highly experienced background in software/AI engineering, managing teams delivering production AI/ML or agentic AI systems. Proven track record shipping enterprise‑grade AI solutions at scale—high availability, low latency, strong security, and compliance. Languages and frameworks: Python, PyTorch/TensorFlow, modern microservices. GenAI/LLMs: prompt engineering, RAG, function/tool calling, agent frameworks, vector databases, embeddings. Data & platforms: modern data stacks, event‑driven designs, experience with major clouds and GPU accelerator workflows. MLOps/LLMOps: CI/CD, model & prompt registries, feature stores, model serving, canary/AB, offline/online evals, observability, cost management. Security & responsible AI: secrets management, IAM, network isolation, policy enforcement; familiarity with content safety tooling and responsible AI practices. UX collaboration: partner with design and research on human‑centered, accessible interfaces for AI‑infused workflows. Experience in payments/financial services or regulated environments highly preferred. Knowledge of PCI DSS, GDPR, ISO 42001, and model risk practices; prior work with sensitive data controls. Multi‑region, active deployments, SLA/SLO design, and incident response leadership. Vendor/platform evaluation and contract oversight for AI tooling and foundation models. Publications, patents, OSS contributions in ML/LLM/agents are a plus. Salary
San Francisco, California: $166,000 – $265,000 USD Remote – New York: $138,000 – $221,000 USD EEO Statement
Mastercard is a merit‑based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disability, veteran status, or any characteristic protected by law. If you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com. The Reasonable Accommodations team will respond promptly. All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organization. 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 confidentiality and integrity, report any suspected violation, and complete all periodic mandatory security trainings in accordance with Mastercard guidelines.
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