The AI Engineering team at Global Payment Holding Company is building and improving ML/DL and Generative AI systems to support automation, personalization, and decision-making across FinTech platforms. This AI Engineer role focuses on delivering production-ready models, agentic workflows, and MLOps capabilities using cloud and AI tooling.
Key Responsibilities
- Design, develop, and deploy machine learning, deep learning, and Generative AI models to address complex business needs and produce measurable outcomes.
- Build and maintain scalable ML pipelines and agentic workflows using modern frameworks and cloud-native tools.
- Work with data scientists, product managers, and engineers to align business requirements with AI-powered solutions.
- Implement and optimize AI solutions using platforms including GCP Vertex AI, AWS Bedrock/SageMaker, and Snowflake Cortex.
- Improve model performance using techniques such as prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and RLHF.
- Develop and deploy autonomous AI agents using frameworks such as LangChain, LangGraph, and AgentSpace.
- Integrate vector databases (for example, PGVector) and LLM orchestration tools to support retrieval and memory in generative systems.
- Apply robust MLOps practices, including CI/CD, monitoring, versioning, and model lifecycle management.
- Stay current with AI research and industry trends, evaluating emerging tools and techniques for enterprise adoption.
- Support internal knowledge sharing through documentation and best practices for responsible and ethical AI development.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 4+ years of experience in AI/ML engineering with a strong foundation in ML/DL algorithms and systems.
- Proficiency in Python and ML libraries including TensorFlow, PyTorch, and Transformers.
- Hands-on experience with Generative AI models (for example, GPT, Mistral, Claude) and agentic AI systems.
- Experience with NLP, conversational AI, and LLM-based applications.
- Familiarity with vector search and semantic retrieval technologies.
- Strong understanding of MLOps, including model deployment, monitoring, and retraining.
- Experience building production-grade AI systems at scale in cloud environments.
- Hands-on experience developing on Google Cloud Platform.
- Strong problem-solving, communication, and collaboration skills.
Technologies and Tools
Python, TensorFlow, PyTorch, Transformers, GCP Vertex AI, AWS Bedrock, SageMaker, Snowflake Cortex, LangChain, LangGraph, AgentSpace, PGVector, CI/CD, RAG (Retrieval-Augmented Generation), RLHF.
About the Team
Our inclusive and global teams win together every day.
What Makes a Globalpayer
- Globalpayers think like a client, act like an owner, and win as one team.
- We are curious and innovative, consistently finding better ways to deliver impact.
- We empower each other to make decisions, with excellence driven by passion.
Location and Work Authorization
Location: Alpharetta, GA (onsite).
Work Authorization: Applicant must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future. No sponsorship is available for H-1B, L-1, TN, O-1, E-3, H-1B1, F-1, J-1, OPT, CPT or any other employment-based visa.
Diversity, EEO, and Additional Requirements
- Global Payments stands against racism, intolerance, and injustice in all forms, and respects, honors, and celebrates the diversity of team members.
- Global Payments is an equal opportunity employer and evaluates qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, or other protected characteristics.
- If a conditional offer of employment is made and you will be working in the United States, a drug test will be required.
- Reasonable accommodations will be provided for individuals with qualified disabilities during the hiring process and to perform essential job functions if hired.
- Washington Candidates: Click here for information on the Fair Chance Act.
Bonus Qualifications
- Experience with prompt engineering, RLHF, and LLM evaluation techniques.
- Understanding of AI governance, safety, and responsible AI principles.
- Familiarity with reinforcement learning, multi-agent systems, and autonomous workflows.
- Experience with big data technologies (for example, Apache Spark, Kafka) and real-time data processing.
- Experience with data engineering techniques and data warehousing platforms such as BigQuery and Snowflake.
- Contributions to open-source AI projects or publications in the AI/ML field.
- Familiarity with CI/CD pipelines, infrastructure-as-code, and cloud-native AI tooling.
- Ability to work proactively with initiative and accuracy, and to manage multiple assignments while meeting deadlines.
- Strong interpersonal skills for professional collaboration with staff and stakeholders.
- Excellent organizational skills, attention to detail, and critical thinking across moderately to highly complex tasks.
- Flexibility to adapt to changing business needs and priorities.
- Ability to work creatively and independently with minimal supervision and use judgment to accomplish goals.
- Experience navigating organizational structures and collaborating across teams.
