Are you excited about building software solutions around large, complex Machine Learning (ML) and Artificial Intelligence (AI) systems? Want to help the largest global enterprises derive business value through the adoption and automation of Generative AI (GenAI)? Excited by using massive amountsof disparate data to develop AI/ML models? Eager to learn to apply AI/ML to a diverse array of enterprise use? Thrilled to be a key part of Amazon, who has been investing in Machine Learning for decades - pioneering and shaping the world's AI technology?
The Amazon Web Services ProfessionalServices (ProServe) team is seeking a skilled ML Engineer to join our team as a Delivery Consultant at Amazon Web Services (AWS). In this role, you'll work closely with customers to design, implement, and manage AWS AI/ML and GenAI solutions that meet their technical requirements and business objectives. You'll be a key player in driving customer success through their cloud journey, providing technical expertise and best practices throughout the ML project lifecycle.
Possessing a deep understanding of AWS products and services, as a Delivery Consultant you will be proficient in architecting complex, scalable, and secure AI/ML and GenAI solutions tailored to meet the specific needs of each customer. You'll work closely with stakeholders to gather requirements, assess current infrastructure, and propose effective migration strategies to AWS. As trusted advisors to our customers, providing guidance on industry trends, emerging technologies, and innovative solutions, you will be responsible for leading the implementation process, ensuring adherence to best practices, optimizing performance, and managing risks throughout the project.
The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using the AWS Cloud. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing initiatives. Our team provides assistance through a collection of offerings which help customers achieve specific outcomes related to enterprise cloud adoption. We also deliver focused guidance through our global specialty practices, which cover a variety of solutions, technologies, and industries.
Key job responsibilities
Key job responsibilities
As an experienced technology professional, you will be responsible for:
1. Implementing end-to-end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring
2. Designing and implementing machine learning pipelines that support high-performance, reliable, scalable, and secure ML workloads
3. Designing scalable ML solutions and operations (MLOps) using AWS services and leveraging GenAI solutions when applicable
4. Collaborating with cross-functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Applications) to prepare, analyze, and operationalize data and AI/ML models
5. Serving as a trusted advisor to customers on AI/ML and GenAI solutions and cloud architectures
6. Sharing knowledge and best practices within the organization through mentoring, training, publication, and creating reusable artifacts
7. Ensuring solutions meet industry standards and supporting customers in advancing their AI/ML, GenAI, and cloud adoption strategies
This is a customer-facing role with potential travel to customer sites as 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 (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
- Experience with AI/ML technologies
- 3+ years of building machine learning and generative AI models for business application experience
- 3+ years of customer-facing work, engaging with customer executives, technologists or partners to solve business problems with advanced technologies experience
- Experience with cloud services related to machine learning (e.g., Amazon SageMaker) and generative AI applications
- 3+ years of coding, data querying languages (e.g. SQL), and scripting languages (e.g. Python)
PREFERRED QUALIFICATIONS
- Knowledge of AWS services including compute, storage, networking, security, databases, machine learning, and serverless technologies
- Knowledge of AWS services including SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch and AWS certifications
- 2+ years of experience with design, deployment, and evaluation of AI agents and orchestration approaches; experience with open source frameworks like LangChain, LangGraph, LlamaIndex, and/ or similar tools
- 3+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience using PyTorch or TensorFlow
- Experience building ML pipelines with MLOps best practices, including: data preprocessing, distributed & GPU training, model deployment, monitoring, and retraining; experience with container and CI/CD pipelines
- Domain expertise in healthcare and life sciences
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 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
USA, GA, Atlanta - 131,300.00 - 177,600.00 USD annually
USA, IL, Chicago - 131,300.00 - 177,600.00 USD annually
USA, NJ, Jersey City - 144,500.00 - 195,400.00 USD annually
USA, NY, New York - 144,500.00 - 195,400.00 USD annually
USA, TX, Austin - 131,300.00 - 177,600.00 USD annually
USA, TX, Dallas - 131,300.00 - 177,600.00 USD annually
USA, TX, Houston - 131,300.00 - 177,600.00 USD annually
USA, VA, Arlington - 131,300.00 - 177,600.00 USD annually
USA, VA, Herndon - 131,300.00 - 177,600.00 USD annually
USA, WA, Seattle - 131,300.00 - 177,600.00 USD annually