AI Engineer at Global Payments

Atlanta, Georgia, United States

Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

NoVisa

FinTech, PaymentsIndustries

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 such as TensorFlow, PyTorch, and Transformers
Hands-on experience with Generative AI models (e.g., 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
Excellent problem-solving, communication, and collaboration skills
Legally authorized to work for any employer in the United States on a full-time basis without the need for current or future immigration sponsorship

Design, develop, and deploy ML, DL, and Generative AI models that solve complex business problems and deliver measurable value
Build and maintain scalable ML pipelines and agentic workflows using modern frameworks and cloud-native tools
Collaborate with data scientists, product managers, and engineers to translate business requirements into AI-powered solutions
Implement and optimize AI solutions using platforms such as GCP Vertex AI, AWS Bedrock/SageMaker, and Snowflake Cortex
Apply techniques such as prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and RLHF to enhance model performance
Develop and deploy autonomous AI agents using frameworks like LangChain, LangGraph, and AgentSpace
Integrate vector databases (e.g., PGVector) and LLM orchestration tools to support retrieval and memory in generative systems
Ensure robust MLOps practices including CI/CD, monitoring, versioning, and lifecycle management of models
Stay current with AI research and industry trends, and evaluate emerging tools and techniques for enterprise adoption
Contribute to internal knowledge sharing, documentation, and best practices for responsible and ethical AI development