DevOps Delivery Consultant, AWS Generative AI Innovation Center

The Generative AI Innovation Center at AWS empowers customers to harness state of the art AI technologies for transformative business opportunities. Our multidisciplinary team of strategists, scientists, engineers, and architects collaborates with customers across industries to fine-tune and deploy customized generative AI applications at scale. Additionally, we work closely with foundational model providers to optimize AI models for Amazon Silicon, enhancing performance and efficiency.

As a DevOps Consultant you will assist customers to launch generative AI workloads on AWS, design and implement distributed model training pipelines or deploy inference at scale. You will provide guidance on automation of infrastructure, applications and machine learning workflows. You will also help customer teams build serverless or containerized applications or adopt DevOps culture and best practices. ou will collaborate with customers and partners in small teams and lead with your expertise to be become customers’ trusted advisor. You will collaborate with customers and partners in small teams and lead with your expertise to be become customers’ trusted advisor, collaborate on internal projects, present on technical events or develop reusable assets, tools, white papers or reference architectures. We will help you get certified. As this is a customer-facing role, we may ask you to travel to customer locations to deliver professional services when needed.

About the team

Diverse Experiences

AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

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) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship & 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.

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.

BASIC QUALIFICATIONS

• Automating real-world production environments with Infrastructure-as-Code (IaC) tools like CloudFormation, CDK, or Terraform, containerization & orchestration (Docker, Kubernetes) and cloud container services (EKS, ECS, Fargate), IP networking, VPNs, DNS, load balancing, and firewalls.
• Continuous integration / delivery (CI / CD) and corresponding tools (i.e such as AWS Code-family, ArgoCD, Jenkins, Bamboo, TeamCity, GitHub actions, etc...), Linux and / or Windows-based systems administration.
• Software development proficiency in at least one scripting / programming language (i.e. Typescript / Python / Java / C++ / Go), with practical experience in automating tasks, building scripts, and solving problems.
• Proven track record of successful implementation of DevOps practices and culture (think observability, code reviews, source control management, building feedback loops, removing organization silos…).
• Basic understanding of Machine Learning concepts related to model training, deployment, inference, MLOps, etc.

PREFERRED QUALIFICATIONS