Machine Learning Engineer , Amazon Customer Service

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. Youll 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 youll build infrastructure that supports the complete lifecycle of AI models - from experimentation and development to production deployment and monitoring.

Youll 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. Come join us on this exciting journey!

Key job responsibilities
- 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 day in the life
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 youll write and review code for AI/ML pipelines generative AI applications and model serving systems while monitoring and optimizing existing AI services for performance accuracy and reliability. Youll often find yourself diving deep into model behavior issues implementing guardrails for responsible AI deployment improving inference latency and throughput and building new capabilities into our AI platforms. Cross-team collaboration is key as you work closely with scientists to translate innovative AI research into production-ready systems and consult with data engineers to ensure high-quality feature and knowledge pipelines. As a senior member of the team youll also mentor junior engineers sharing your expertise in AI system design and best practices.

About the team
The Data Intelligence team is a new function within Customer Engagement Technology. We own the end-to-end process of defining building implementing and monitoring a comprehensive data and AI strategy. We also develop and apply Generative Artificial Intelligence (GenAI) Large Language Models (LLMs) Computer Vision ML Knowledge Graphs and Natural Language Processing (NLP) to customer service associate experiences and foundational technologies.

- 3 years of contributing to new and current systems architecture and design (architecture design patterns reliability and scaling) experience
- Experience with Machine Learning and Large Language Model fundamentals including architecture training/inference lifecycles and optimization of model execution
- Experience in machine learning data mining information retrieval statistics or natural language processing

- 3 years of full software development life cycle including coding standards code reviews source control management build processes testing and operations experience
- Masters degree in computer science or equivalent
- Experience in developing and deploying LLMs in production on GPUs Neuron TPU or other AI acceleration hardware

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

The base salary range for this position is listed below. As a total compensation company Amazons package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon offers comprehensive benefits including health insurance (medical dental vision prescription basic life & AD&D insurance) Registered Retirement Savings Plan (RRSP) Deferred Profit Sharing Plan (DPSP) paid time off and other resources to improve health and well-being. We thank all applicants for their interest however only those interviewed will be advised as to hiring status.

CAN BC Vancouver - 114800.00 - 191800.00 CAD annually