Machine Learning Engineer, GenAI, Amazon Connect

As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use.

Amazon Connect is an AI-powered customer experience solution that enables superior outcomes at a lower cost. Since its 2017 public launch, Amazon Connect has become an AI leader, transforming how organizations of all types interact with their customers.

Do you want to build and optimize the infrastructure that serves frontier Large Language Models (LLMs) at massive scale, transforming how customers interact with AI-powered services? Join a world-class team of ML engineers and scientists within AWS to develop production ML systems that power next-generation applications in cloud computing.

Amazon Web Services (AWS) is the world’s leading cloud platform, supporting millions of customers globally. Our customers bring complex, high-impact problems that create unique opportunities for Machine Learning Engineers to deliver solutions with immediate, real-world impact. You will operate as a technical leader, owning the design and evolution of large-scale ML infrastructure. You will partner closely with applied scientists, software engineers, and product teams to translate frontier LLM research into highly reliable, efficient, and scalable production systems. You will work with state-of-the-art GPU and custom accelerator hardware, and leverage AWS’s unmatched scale in data and compute to push the boundaries of LLM serving and optimization.

As part of the team, we expect that you will design and build highly available, cost-efficient LLM serving systems, optimize inference performance across the full stack, and develop innovative ML infrastructure solutions that enable our scientists to iterate faster and our customers to experience AI capabilities at their best.

Key job responsibilities
Our machine learning engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You'll bring a passion for innovation, large language models, inference optimization, distributed systems, and cloud-native ML infrastructure. You'll also:

* Design, develop, and research machine learning systems end-to-end — building robust ML solutions that translate data science prototypes into production-ready systems that drive real business outcomes.
* Build, host, and maintain production-grade LLM serving and inference infrastructure — delivering high-quality, highly available, always-on AI systems that customers and internal teams can depend on.
* Optimize the full inference stack for performance and cost-efficiency — applying techniques such as model quantization, batching strategies, KV-cache management, and accelerator tuning.
* Partner with cross-functional teams and customers to deeply understand real-world challenges, and iteratively translate requirements into scalable, secure, and cost-effective machine learning solutions on AWS.



About the team
Why AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE), inspire us to never stop embracing our uniqueness.

Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Mentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skil