AI Support Engineer (II+)

Global Payments Inc.•Corpus Christi, TX

About The Position

Every day, Global Payments makes it possible for millions of people to move money between buyers and sellers using our payments solutions for credit, debit, prepaid and merchant services. Our worldwide team helps over 3 million companies, more than 1,300 financial institutions and over 600 million cardholders grow with confidence and achieve amazing results. We are driven by our passion for success and we are proud to deliver best-in-class payment technology and software solutions. Join our dynamic team and make your mark on the payments technology landscape of tomorrow. At this time, we are unable to offer visa sponsorship for this position. Candidates must be legally authorized to work for any employer in the United States (or (applicable country) on a full-time basis without the need for current or future immigration sponsorship. At Global Payments (NYSE : GPN), we empower simple, fast, secure payments for everyone. And to do that, we’ve built a complete, worldwide commerce ecosystem that delivers software and services at scale to millions of companies around the world—from ambitious startups to global enterprises. Our team of 27,000 experts is dedicated to your success. Headquartered in Atlanta, Georgia, we're a Fortune 500® company and a member of the S&P 500 with worldwide reach spanning North America, Europe, Asia Pacific and Latin America. Wherever you are—or wherever you want to be—we’re there, ready to support you. For more information, visit company.globalpayments.com and follow Global Payments on X, LinkedIn and Facebook.

Requirements

4+ years of experience in production support, software engineering, site reliability engineering (SRE), or DevOps—preferably supporting GenAI and/or ML systems.
Strong understanding of cloud infrastructure (AWS, GCP) and AI observability tools (e.g., Fiddler AI, Arize AI, IBM WatsonX.governance, etc.).
Experience with LLM and GenAI systems (OpenAI, Azure OpenAI, Bedrock, Vertex AI, or similar).
Familiarity with modern orchestration and agentic frameworks such as LangChain, LangGraph, Autogen, or CrewAI.
Proficiency in Python or shell scripting for automation and troubleshooting.
Strong analytical, communication, and incident management skills.
Bachelor’s degree in Computer Science, Engineering, or a related field.
1+ years of experience in AI/ML engineering, with a focus on Generative AI.
Proficiency in programming languages such as Python.
Strong understanding of Generative AI models (e.g., GPT, Transformer architectures) and experience in distilling, tuning and training them.
Familiarity with Retrieval Augmented Generation (RAG) techniques and their implementation.
Experience with agentic AI concepts and developing autonomous AI workflows.
Hands-on experience with GCP Vertex AI, AWS Bedrock + Sagemaker, and Snowflake Cortex platforms and their AI/ML capabilities.
Experience building production-grade AI/ML systems at scale.
Knowledge of MLOps practices, including model deployment and lifecycle management.
Excellent problem-solving and analytical skills.
Excellent communication and collaboration skills.
Availability for on-call rotation and support.
Ability to work proactively with a high level of initiative and accuracy.
Ability to manage multiple assignments effectively and meet established deadlines.
Strong interpersonal skills to interact professionally with staff and stakeholders.
Excellent organizational skills and attention to detail.
Critical thinking ability ranging from moderately to highly complex tasks.
Flexibility in adapting to changing business needs and priorities.
Ability to work creatively and independently with minimal supervision.
Ability to utilize experience and judgment in accomplishing goals.
Experience in navigating organizational structures and collaborating across teams.

Nice To Haves

Familiarity with Prompt Engineering, RLHF, and model evaluation techniques.
Understanding of AI governance, safety, and responsible principles.
Understanding of reinforcement learning and its application in agentic AI.
Familiarity with big data technologies (Apache Spark, Kafka).
Experience with CI/CD tools and automation for AI/ML workflows.
Experience with real-time data processing and streaming analytics.

Responsibilities

Serve as the first line of defense for production AI incidents, ensuring rapid triage, root cause analysis, and resolution.
Monitor system health and performance of deployed AI applications, agentic and RAG-based solutions, MCPs, and orchestration platforms.
Track and investigate issues related to latency, failures, model drift, hallucination, prompt misbehavior, or broken integrations, escalating to the AI engineering group where appropriate.
Collaborate with AI and platform engineers to implement observability, logging, and alerting best practices for all AI services.
Build diagnostic tools, runbooks, and automated workflows to improve incident response time and reduce manual intervention.
Maintain knowledge bases and playbooks for repeatable troubleshooting and knowledge transfer.
Partner with governance and compliance teams to ensure incidents are documented and remediated in line with internal policy.
Contribute to postmortems and continuous improvement efforts to harden production systems.