AI Engineer

Global Payment Holding Company•Alpharetta, OK

11d

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. Ready to take your career global? Make your mark at one of the biggest names in payments. We are seeking a highly skilled and forward-thinking AI Engineer to join our AI Engineering team. This role is ideal for a hands-on technologist with deep expertise in machine learning (ML) and deep learning (DL), and a strong working knowledge of Generative AI technologies. You will design, build, and optimize intelligent systems that power automation, personalization, and decision-making across our FinTech platforms. This is a unique opportunity to work at the intersection of cutting-edge AI research and real-world enterprise applications.

Requirements

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.
Hands-on experience with development on Google Cloud Platform.
Excellent problem-solving, communication, and collaboration skills.

Nice To Haves

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 (e.g., Apache Spark, Kafka) and real-time data processing.
Experience with data engineering techniques and data warehousing platforms (e.g. BigQuery, 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 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.

Responsibilities

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.