The Amazon Web Services Professional Services (ProServe) team is seeking a skilled ML Engineer to join our team as a Delivery Consultant at Amazon Web Services (AWS). In this role, youll work closely with customers to design, implement, and manage AWS AI/ML and GenAI solutions that meet their technical requirements and business objectives. Youll 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. Youll 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.
BASIC QUALIFICATIONS
- 3+ years of cloud architecture and solution implementation experience
- 3+ years data, software, or ML engineering, with understanding of distributed computing (e.g., data pipelines, training and inference, ML infrastructure design)
- 3+ years developing predictive modeling, natural language processing, and deep learning, with experience in building and deploying ML models on cloud (e.g., Amazon SageMaker or similar)
- 3+ years developing with SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, TypeScript)
PREFERRED QUALIFICATIONS
- Knowledge of AWS services including compute, storage, networking, security, databases, machine learning, and serverless technologies
- AWS experience preferred, with proficiency in a range of AWS services (e.g., SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, Step Functions, VPC, CloudFormation)
- Experience with automation (e.g., Terraform, Python), Infrastructure as Code (e.g., CloudFormation, CDK), and Containers & CI/CD Pipelines
- Experience building ML pipelines with MLOps best practices, including: data preprocessing, model hosting, feature selection, hyperparameter tuning, distributed & GPU training, deployment, monitoring, and retraining
- Experience with MLOps (e.g., MLFlow, Kubeflow) and orchestration (e.g., Airflow, AWS Step Functions). Experience building applications using GenAI technologies (LLMs, Vector Stores, LangChain, Prompt Engineering)
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, CA, San Diego - 131,300.00 - 177,600.00 USD annually
USA, CA, San Francisco - 151,000.00 - 204,300.00 USD annually
USA, GA, Atlanta - 131,300.00 - 177,600.00 USD annually
USA, IL, Chicago - 131,300.00 - 177,600.00 USD annually
USA, MA, Boston - 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, Dallas - 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
