AI & Data Platform Engineer

• Build and evolve scalable AI platform capabilities that enable teams to train, deploy, monitor and scale artificial intelligence and machine learning solutions reliably, securely and efficiently.
• Accelerate AI delivery from experimentation to production by creating automated model deployment, data and feature pipelines, evaluation workflows and continuous integration and continuous delivery (CI/CD) capabilities that improve speed, consistency and quality.
• Strengthen the reliability of production AI through robust observability, model monitoring, logging and evaluation capabilities that help teams identify issues faster, maintain performance and confidently operate AI at scale.
• Shape the foundation for generative AI innovation by advancing model serving, application programming interfaces (APIs), prompt and Retrieval-Augmented Generation (RAG) workflows and vector infrastructure that enable teams to build new AI-powered experiences.
• Optimize the performance and economics of AI at scale by influencing platform architecture, cloud infrastructure and engineering standards that improve efficiency, security, resilience and cost effectiveness.

• 4 to 8 years of hands-on experience building, automating, deploying or operating AI and machine learning platforms, with demonstrated success supporting production environments.
• Strong engineering capabilities with Python, Structured Query Language (SQL), cloud services, Docker, Kubernetes, CI/CD and infrastructure as code, using technologies such as Terraform or equivalent solutions.
• Proven experience with machine learning operations (MLOps), large language model operations (LLMOps), model lifecycle tooling such as MLflow, model serving, APIs, monitoring, logging and production troubleshooting.
• Experience designing and operating data pipelines, distributed processing and scalable cloud-based architectures, with the judgment to solve complex technical challenges and improve platform reliability.
• A relevant degree in quantitative analytics, statistics, mathematics, data science or a related discipline, combined with a collaborative and innovative mindset and the ability to influence technical decisions across teams., * Experience with product analytics and using data and insights to influence product or platform decisions.
• A master’s degree in quantitative analytics, statistics, mathematics, data science or a related discipline.
• Experience with analytics, distributed data or visualization technologies such as Spark, Hadoop, Power BI, Tableau or similar platforms.

Globalpayers think like a client, act like an owner and win as one team. We’re curious and innovative - always finding better ways to deliver impact. We empower each other to make decisions, and it’s our passion that drives excellence in everything we set out to do.