Machine Learning Engineer, Professional Services AWS Industries

Key Skills

Amazon Simple Storage Service (S3), AWS, Computer Vision (CV), Deep Learning, Machine learning techniques, Natural Language Processing (NLP)

Job Description

This is a team works step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. This team provides guidance on best practices for applying generative AI responsibly and cost efficiently.

You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.

As an ML Engineer, you'll partner with technology and business teams to build solutions that surprise and delight our customers. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies.

We’re looking for Engineers and Architects capable of using generative AI and other ML techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

Key job responsibilities

• Collaborate with ML scientist and engineers to research, design and develop cutting-edge generative AI algorithms to address real-world challenges
• Work across customer engagement to understand what adoption patterns for generative AI are working and rapidly share them across teams and leadership
• Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths for generative AI
• Create and deliver reusable technical assets that help to accelerate the adoption of generative AI on AWS platform
• Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder
• Provide customer and market feedback to Product and Engineering teams to help define product direction.

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

• 7+ years of technical specialist, design and architecture experience
• 5+ years of software development with object oriented language experience
• 3+ years of cloud based solution (AWS or equivalent), system, network and operating system experience

PREFERRED QUALIFICATIONS

• Bachelor’s degree in computer science or equivalent with 8+ years of relevant working experience, or Master’s degree in computer science or equivalent with 5+ years of working experience
• Experience related to machine learning, deep learning, NLP, CV, GNN, or distributed training
• Experience related to AWS services such as SageMaker, EMR, S3, DynamoDB and EC2
• Working knowledge of generative AI and hands on experience in prompt engineering, deploying and hosting Large Foundational Models