Mastercard
Overview
Mastercard’s AI Center of Excellence (AI COE) is seeking an AI Engineering Manager to lead a team building production grade Agentic AI and traditional AI/ML solutions across Mastercard’s business. You will 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 is a hands-on leadership role that 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 (LLM/ML, full stack, platform/MLOps, LLMOPs, evals) with clear growth paths, coaching, and inclusive practices. Establish engineering rituals (design reviews, postmortems, chapter forums) 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 POC 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 Mastercard’s Business. Implement RAG, function/tool calling, knowledge graph integrations, and domain adapters for enterprise contexts. Stand up evaluation frameworks (offline/online, human in the loop) 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 on pipelines, Legal and Data & AI Governance teams for data contracts, and Data product managers for high quality, policy compliant datasets. Embed privacy by design, data minimization, and financial services grade security into architectures. Collaborate with Risk, Compliance, and Legal to meet obligations (e.g., PCI DSS, GDPR, SOC 2, ISO 42001), and to operationalize Responsible AI (transparency, fairness, human oversight, auditability). Establish model risk management processes. Partner with Product Managers to define outcomes, prioritize roadmaps, and validate user value through experimentation. Translate complex technical tradeoffs for 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: Builds inclusive, high trust teams; coaches engineers; sets clear goals; recognizes excellence. Delivery Excellence: Plans, sequences, and executes complex programs; removes roadblocks; delivers reliably. Technical Judgment: Weighs build/buy performance vs. cost, guardrails vs. UX; anticipates risks early. Customer Obsession: Anchors solutions in measurable user and business outcomes. Communication: Explains complex concepts simply; adapts message to executives, partners, and engineers. Adaptability: Learns fast, iterates, and scales 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, including multiple years managing engineering 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/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 one or more 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/guardrail tooling and Responsible AI practices. UX Collaboration: Ability to partner with design and research on human centered, accessible interfaces for AI infused workflows. Experience in payments/financial services or similarly regulated environments is highly preferred. Knowledge of PCI DSS, GDPR, ISO 42001, and model risk practices; prior work with sensitive data controls (PII, tokenization, redaction). 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. Benefits and Other Details
Mastercard is a merit-based, inclusive, equal opportunity employer. Reasonable accommodations are available upon request in the US or Canada. Corporate Security responsibilities apply to all Mastercard employees, including adherence to security policies and mandatory trainings. The company offers a competitive base salary and benefits, with pay ranges by location (examples include: San Francisco, Boston, New York City, Purchase, Remote – New York). Pay Ranges (illustrative): San Francisco: $166,000 - $265,000; Boston: $159,000 - $254,000; New York City: $166,000 - $265,000; Purchase: $159,000 - $254,000; Remote - New York: $138,000 - $221,000. Job Posting Window: Posting windows may change; candidates are encouraged to apply expeditiously.
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Mastercard’s AI Center of Excellence (AI COE) is seeking an AI Engineering Manager to lead a team building production grade Agentic AI and traditional AI/ML solutions across Mastercard’s business. You will 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 is a hands-on leadership role that 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 (LLM/ML, full stack, platform/MLOps, LLMOPs, evals) with clear growth paths, coaching, and inclusive practices. Establish engineering rituals (design reviews, postmortems, chapter forums) 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 POC 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 Mastercard’s Business. Implement RAG, function/tool calling, knowledge graph integrations, and domain adapters for enterprise contexts. Stand up evaluation frameworks (offline/online, human in the loop) 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 on pipelines, Legal and Data & AI Governance teams for data contracts, and Data product managers for high quality, policy compliant datasets. Embed privacy by design, data minimization, and financial services grade security into architectures. Collaborate with Risk, Compliance, and Legal to meet obligations (e.g., PCI DSS, GDPR, SOC 2, ISO 42001), and to operationalize Responsible AI (transparency, fairness, human oversight, auditability). Establish model risk management processes. Partner with Product Managers to define outcomes, prioritize roadmaps, and validate user value through experimentation. Translate complex technical tradeoffs for 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: Builds inclusive, high trust teams; coaches engineers; sets clear goals; recognizes excellence. Delivery Excellence: Plans, sequences, and executes complex programs; removes roadblocks; delivers reliably. Technical Judgment: Weighs build/buy performance vs. cost, guardrails vs. UX; anticipates risks early. Customer Obsession: Anchors solutions in measurable user and business outcomes. Communication: Explains complex concepts simply; adapts message to executives, partners, and engineers. Adaptability: Learns fast, iterates, and scales 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, including multiple years managing engineering 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/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 one or more 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/guardrail tooling and Responsible AI practices. UX Collaboration: Ability to partner with design and research on human centered, accessible interfaces for AI infused workflows. Experience in payments/financial services or similarly regulated environments is highly preferred. Knowledge of PCI DSS, GDPR, ISO 42001, and model risk practices; prior work with sensitive data controls (PII, tokenization, redaction). 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. Benefits and Other Details
Mastercard is a merit-based, inclusive, equal opportunity employer. Reasonable accommodations are available upon request in the US or Canada. Corporate Security responsibilities apply to all Mastercard employees, including adherence to security policies and mandatory trainings. The company offers a competitive base salary and benefits, with pay ranges by location (examples include: San Francisco, Boston, New York City, Purchase, Remote – New York). Pay Ranges (illustrative): San Francisco: $166,000 - $265,000; Boston: $159,000 - $254,000; New York City: $166,000 - $265,000; Purchase: $159,000 - $254,000; Remote - New York: $138,000 - $221,000. Job Posting Window: Posting windows may change; candidates are encouraged to apply expeditiously.
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