Machine Learning Engineer , Amazon Customer Service
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Key skills for this role
About the Role
Amazon Customer Service is seeking a Machine Learning Engineer to design and build robust, scalable AI/ML systems and infrastructure. The role involves architecting end-to-end AI pipelines, developing generative AI solutions, and collaborating with cross-functional teams to create enterprise-scale AI/ML systems.
Key Skills for This Role
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
Requirements
- 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 (preferred)
- Master's degree in computer science or equivalent (preferred)
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware (preferred)
Full Job Posting
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.
- 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 bu
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.
Basic Qualifications
- 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
Preferred Qualifications
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Master's degree in computer science or equivalent
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
Compensation
- The base salary range for this position is 114,800.00 191,800.00 CAD annually.
- Amazon's package may include other elements such as sign on payments and restricted stock units (RSUs).
- 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.
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