Delivery Consultant- AI/ML, Data & Machine Learning (DML)

Amazon Web Services (AWS)

Arlington (VA)

On-site

USD 131,300 - 177,600

Full time

14 days+

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Benefits offered by this job

Health insurance
401(k) matching
Paid time off

Job summary

Amazon Web Services (AWS) ProServe is seeking a Machine Learning Engineer to design, evangelize, implement, and scale AI/ML solutions for customers. You will work directly with clients to align technical requirements with business objectives and drive successful AI transformations.

The role requires deep expertise in ML, Generative AI, and best practices across project lifecycles, with a focus on scalable and secure cloud solutions on AWS.

Qualifications

  • 3+ years of ML/statistical modeling, data analysis tools and performance parameters.
  • 2+ years of cloud architecture and solution implementation experience.
  • Experience in SDLC, coding standards, software architectures, code reviews, source control, CI/CD, testing and operations.
  • Experience in defining benchmarks for GenAI model performance.
  • Experience applying quantitative analysis to solve business problems across multi-team projects.

Responsibilities

  • Design, implement scalable AI/ML solutions on AWS tailored to customer needs, selecting and fine-tuning models.
  • Develop and deploy ML models and GenAI apps, optimizing performance at scale.
  • Collaborate with stakeholders to identify high-value AI/ML use cases, gather requirements, and propose strategies.
  • Provide guidance on applying AI/ML responsibly and cost-efficiently, ensuring best practices.
  • Advise customers on latest AI/ML advancements and data strategies; mentor teammates and create reusable AI artifacts.
  • Share knowledge within the organization, mentor, and prototype new technologies.

Skills

Machine Learning
Cloud Architecture
Software Engineering
GenAI Benchmarks
Cross-functional Collaboration

Education

Master's degree or above (STEM)

Tools

AWS
Serverless

Job description

The Amazon Web Services Professional Services (ProServe) team is seeking a skilled Machine Learning Engineer to join our team at Amazon Web Services (AWS). Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? In this role, you'll work directly with customers to design, evangelize, implement, and scale AI/ML solutions that meet their technical requirements and business objectives. You'll be a key player in driving customer success through their AI transformation journey, providing deep expertise in machine learning, generative AI, and best practices throughout the project lifecycle.

This position requires that the candidate selected be a US Citizen and must currently possess and maintain an active TS/SCI security clearance.

Key job responsibilities
  • Designing and implementing complex, scalable, and secure AI/ML solutions on AWS tailored to customer needs, including selecting and fine-tuning appropriate models for specific use cases
  • Developing and deploying machine learning models and generative AI applications that solve real-world business problems, conducting experiments and optimizing for performance at scale
  • Collaborating with customer stakeholders to identify high-value AI/ML use cases, gather requirements, and propose effective strategies for implementing machine learning and generative AI solutions
  • Providing technical guidance on applying AI, machine learning, and generative AI responsibly and cost-efficiently, troubleshooting throughout project delivery and ensuring adherence to best practices
  • Acting as a trusted advisor to customers on the latest advancements in AI/ML, emerging technologies, and innovative approaches to leveraging diverse data sources for maximum business impact
  • Sharing knowledge within the organization through mentoring, training, creating reusable AI/ML artifacts, and working with team members to prototype new technologies and evaluate technical feasibility
Basic Qualifications
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
  • 2+ years of cloud architecture and solution implementation experience
  • Experience in professional software engineering & best practices for the full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
  • Experience in defining and creating benchmarks for assessing GenAI model performance
  • Experience applying quantitative analysis to solve business problems and working on multi-team, cross-disciplinary projects
Preferred Qualifications
  • Knowledge of AWS services including compute, storage, networking, security, databases, machine learning, and serverless technologies
  • Experience in performance optimization and cost management for cloud environments
  • Master's degree or above in Science, Technology, Engineering, or Mathematics (STEM)
  • Experience translating technical aspects of analysis and metrics into actionable insights for the advertiser and both technical and non-technical stakeholders
About The Team
Diverse Experiences

Amazon 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. 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.

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.

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.

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.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, VA, Arlington - 131,300.00 - 177,600.00 USD annually

USA, VA, Herndon - 131,300.00 - 177,600.00 USD annually

Company

Amazon Web Services, Inc.

Job ID: A10471795

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