Company Description

Brand Payments provides credit card issuers with expertise in technical and processor-related initiatives. The company delivers services across consumer and commercial card products, such as platform modernization, processor conversions, product builds & management, and card program assessments. They address issuer-specific needs for platform integrations, product optimization, operational technology, competitive functionality, and RFP support for card-related software. Brand Payments focuses on providing tailored solutions to meet client needs and drive innovation in the card payments space.

Overview

We are seeking an AI Engineer to accelerate the modernization and cloud migration of a legacy commercial card processing platform. This role will design and implement AI solutions that improve internal productivity and embed intelligent features into the next‑generation card platform. The engineer will partner closely with product, engineering, data, and operations teams to drive both internal efficiency and external customer value.

AI enablement for engineering teams

• Enable effective use of approved AI-assisted development tools such as enterprise copilots, internal GenAI tooling, and approved LLM platforms.
• Develop and maintain reusable prompt patterns, agent workflows, and repository-level guidance to standardize AI-assisted development across teams.
• Partner with internal AI governance and architecture groups to pre-align use cases, reduce approval friction, and avoid rework.

Legacy analysis and modernization acceleration

• Apply AI-assisted techniques to analyze legacy codebases, extract business rules, and document undocumented behavior.
• Support decomposition of monolithic applications into domain-aligned, API-first architectures using AI-driven dependency analysis.
• Assist teams modernizing legacy stacks including mainframe, batch, and large Java-based systems.

Testing and quality acceleration

• Enable AI-assisted generation of unit, integration, contract, and regression tests.
• Help integrate AI-generated tests into existing CI/CD pipelines without compromising quality, compliance, or auditability.
• Improve test coverage and reduce manual testing toil across modernization programs.

Education and cross-training

• Act as a hands-on coach for engineers and tech leads on compliant, effective AI usage.
• Create lightweight training materials, examples, and playbooks tailored to real delivery scenarios.
• Embed with teams temporarily to drive adoption, not just documentation.

Governance and risk alignment

• Ensure AI usage aligns with security, data privacy, PCI, and internal risk requirements.
• Work closely with architecture, security, and risk partners early in the lifecycle to avoid late-stage blockers.

Required qualifications

• Strong background in software engineering with hands-on experience modernizing large, legacy systems.
• Practical experience using AI tools for code analysis, generation, documentation, or testing.
• Understanding of CI/CD pipelines, automated testing, and API-based architectures.
• Proven ability to operate effectively in regulated, risk-driven enterprise environments.
• Strong communication skills and the ability to teach and influence without formal authority.
• Hands-on experience with cloud platforms (Azure/AWS/GCP) and MLOps tooling.

Preferred qualifications

• Experience modernizing Java-based monoliths into Spring Boot or similar API architectures.
• Experience with COBOL, mainframe environments, or large-scale batch processing systems.
• Familiarity with containerized or cloud-based deployment models.
• Knowledge of PCI, model risk governance (MRM), and banking compliance controls.
• Observability stack experience.
• Experience with commercial card, credit systems, or transaction authorization platforms.
• 5+ years of experience in AI/ML engineering or applied machine learning.
• Strong proficiency with Python, embeddings, vector stores, transformers, and LLM frameworks.
• Exposure to microservices, event-driven architectures, and high-volume systems (Kafka, Kubernetes, etc.).

Success measures

• Reduced time spent by teams on legacy analysis, documentation, and test creation.
• Increased, compliant adoption of AI-assisted development practices.
• Faster modernization throughput without increased production or compliance risk.
• Clear evidence of reusable patterns, prompts, and workflows adopted by multiple teams.
• AI-enhanced product features launched successfully on the next‑gen issuing platform.
• Strong compliance, model governance, and audit readiness.