Machine Learning Engineer , Customer Service

Description

We are looking for a Machine Learning Engineer on the Data Intelligence team which is part of Amazon Customer Service (CS) team, you will design and build robust, scalable AI/ML systems and infrastructure. You'll architect end-to-end AI pipelines for model training, evaluation, and deployment, implement secure and efficient data processing solutions, and develop production‑grade AI services including generative AI, large language models (LLMs), and intelligent agent systems.

Additionally, you'll build infrastructure that supports the complete lifecycle of AI models - from experimentation and development to production deployment and monitoring.

We are looking for a Machine Learning Engineer on the Data Intelligence team which is part of Amazon Customer Service (CS) team, you will design and build robust, scalable AI/ML systems and infrastructure. You'll architect end-to‑end AI pipelines for model training, evaluation, and deployment, implement secure and efficient data processing solutions, and develop production‑grade AI services including generative AI, large language models (LLMs), and intelligent agent systems.

Additionally, you'll build infrastructure that supports the complete lifecycle of AI models - from experimentation and development to production deployment and monitoring. You'll work with cross‑functional teams (e.g., scientists, product managers, data engineers) to create enterprise‑scale AI/ML systems that handle high‑volume inference workloads, implement comprehensive model and AI governance frameworks, and build scalable AI‑powered products that power critical business capabilities.

If you enjoy solving complex AI and machine learning challenges in high‑scale environments, working in a collaborative and dynamic team, and want to make a lasting impact on Amazon Customer Service worldwide, this is your opportunity.

• Design and implement enterprise‑scale AI/ML pipelines and model serving infrastructure that ensure optimal performance, reliability, and low‑latency inference for both traditional ML models and generative AI systems.
• Architect and build AI platform infrastructure that supports the complete model lifecycle, from training environments, feature stores, and validation frameworks to production deployment, A/B testing, and monitoring systems.
• Develop and deploy generative AI solutions, including LLM‑based applications, retrieval‑augmented generation (RAG) systems, AI agents, and intelligent automation workflows.
• Build and optimize AI model serving systems for production use, including model compression, quantization, prompt engineering pipelines, and efficient serving strategies to meet latency and throughput requirements.
• Develop and maintain robust AI governance frameworks, implementing security controls, guardrails, responsible AI practices, and compliant data access patterns that protect sensitive information.
• Drive technical architecture decisions and system design, focusing on scalability, reliability, and performance of distributed AI/ML services while ensuring alignment with business requirements.
• Own end‑to‑end delivery of AI/ML solutions, including design, implementation, experimentation, and verification of components, using standard software engineering and AI/ML engineering methodologies and best practices.
• Collaborate with cross‑functional teams, including Product Managers, Applied Scientists, and Data Engineers, to understand requirements, conduct design reviews, and ensure successful delivery of AI solutions while maintaining high development standards.

A typical day as a Machine Learning Engineer involves architecting and building robust AI/ML infrastructure and intelligent systems that power critical AI initiatives. Your morning might start with reviewing model performance metrics and experiment results, collaborating with Applied Scientists to optimize LLM prompting strategies or model architectures, or working with Product Managers to plan AI product features. Throughout the day, you'll write and review code for AI/ML pipelines, generative AI applications, and model serving systems, while monitoring…