Senior AI Platform Engineer – Scalable & Secure

Mastercard Colombia, Inc.

Atlanta (GA)

On-site

USD 115,000 - 184,000

Full time

9 days ago

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Job summary

Mastercard is seeking a Senior Software Engineer to design and build production-grade AI platform capabilities enabling AI-powered products and agentic experiences. You will craft reusable platform services, orchestration and runtime capabilities, and integration patterns that let AI models, agents, tools, enterprise data, and services run safely and observably in customer-facing products.

You will partner with AI Engineers, Data Scientists, Product Managers, and platform teams, owning identity,

Qualifications

Experience building distributed, production-grade platforms or services.
Strong OO programming and backend development skills (Java, Kotlin, Python).
Solid software design, architecture, data structures, and performance engineering.
Experience designing APIs, platform services, event-driven integrations, and async workflows.
Deep understanding of authentication, authorization, secure service-to-service communication, and policy enforcement.
Experience operating high-reliability, observable systems with regulatory considerations.
Ability to work in ambiguous problem spaces and evolve platform capabilities.
Experience integrating LLMs or AI services into production systems.
Familiarity with agent frameworks, model gateways, and orchestration platforms.

Responsibilities

Design scalable, resilient AI platform services supporting AI-powered products and workflows.
Create secure integration patterns for models, agents, tools, and data so actions are attributable and auditable.
Implement orchestration and runtime patterns for multi-step AI execution and asynchronous processing.
Build reusable frameworks for model/tool invocation and context management with robust error handling.
Integrate identity, permissions, policy enforcement, evaluation, and guardrails into AI execution paths.
Ensure Mastercard standards for reliability, security, compliance, scalability and performance.
Drive unit, functional, and integration testing to validate platform behavior.
Collaborate with AI Engineers and Data Scientists to integrate models and evaluation approaches.
Own operations: logging, monitoring, tracing, incident response and continuous improvement.
Review code, mentor peers, and contribute reusable engineering standards.