Technical Account Manager (UAE), Enterprise Support - EMEA Startups

Amazon
London
1 month ago
Applications closed

Related Jobs

View all jobs

Campaign Data Analyst (PEGA, Adobe or Unica)

Senior Data Analyst (Project Controls)

Technical Engineer

Senior Machine Learning Product Manager (Deploy)

eDiscovery Litigation Data Analyst (Remote)

Paid Search Lead

Technical Account Manager (UAE), Enterprise Support - EMEA Startups At Amazon, our vision is to be earth’s most customer-centric company. In 2006, we launched Amazon Web Services, giving customers access to the same cloud technology we built to serve millions of shoppers on Amazon.com. Amazon Web Services (AWS) is a secure cloud services platform, offering computing power, database storage, content delivery, and other functionality to help businesses scale and grow.

Is this the role you are looking for If so read on for more details, and make sure to apply today.About AWSDiverse ExperiencesAWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply.

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 CultureHere at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empowers us to be proud of our differences.

Mentorship & Career GrowthWe’re continuously raising our performance bar as we strive to become Earth’s Best Employer.

Work/Life BalanceWe value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home.

The RoleAn AWS Technical Account Manager is a trusted advisor and cloud operations architect for our Enterprise Support customers. You’ll craft and execute strategies to drive our customers’ adoption and use of AWS services. This includes a range of products including EC2, S3, DynamoDB & RDS databases, Lambda, CloudFront CDN, IoT and many more.

Key job responsibilities:You’ll advise on solutions, provide technical guidance and advocate for the customer.Ensure AWS environments remain operationally healthy whilst reducing cost and complexity.Develop trusting relationships with customers, understanding their business needs and technical challenges.Using your technical acumen and customer obsession, you’ll drive technical discussions regarding incidents, trade-offs, and risk management.Consult with a range of partners from developers through to C-suite executives.Collaborate with AWS Solutions Architects, Business Developers, Professional Services Consultants, and Sales Account Managers.Proactively find opportunities for customers to gain additional value from AWS.Provide detailed reviews of service events, monthly & quarterly metrics, detailed pre-launch planning.Solve a variety of problems across different customers as they migrate their workloads to the cloud.Uplift customer capabilities by running workshops, brown bag sessions, etc.

About the teamAs we continue to rapidly expand in EMEA, you’ll have plenty of opportunities to develop your technical, consulting and leadership skills.

Do you want to be part of history and transform businesses through cloud computing adoption? We would love to hear from you.

Minimum Qualifications:Experience in a similar role as a Technical Account Manager, Consultant, Solutions Architect, Platform Engineer, Systems Engineer, Cloud Architect, etc.Understand operational parameters and troubleshooting for a combination of the following: Compute, Storage, Networking, CDN, Databases, DevOps, Big Data and Analytics, Security, Applications Development.Internal enterprise or external customer-facing experience with the ability to clearly articulate to small and large audiences.Ability to juggle tasks and projects in a fast-paced environment.Customer obsessed.

Preferred Qualifications:Professional experience with cloud offerings such as AWS, Azure, Google Cloud Platform, etc.Programming or scripting skills with a combination of Java, Python, Perl, Ruby, C#, and/or PHP a plus but not a requirement.Previous experience as a Software Engineer, Developer, DevOps Engineer, etc.Understanding of DevOps practices and tools including Continuous Integration / Deployment, Puppet, Docker, Kubernetes, Chef is a plus.

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 here for more information.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.