Delivery Consultant - Machine Learning Engineer, AWS Professional Services

Amazon.com-posted 6 months ago
$118,200 - $204,300/Yr
Mid Level
Arlington, VA
5,001-10,000 employees
General Merchandise Retailers
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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 amounts of 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 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, 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.

  • Implementing end-to-end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring.
  • Designing and implementing machine learning pipelines that support high-performance, reliable, scalable, and secure ML workloads.
  • Designing scalable ML solutions and operations (MLOps) using AWS services and leveraging GenAI solutions when applicable.
  • Collaborating with cross-functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Applications) to prepare, analyze, and operationalize data and AI/ML models.
  • Serving as a trusted advisor to customers on AI/ML and GenAI solutions and cloud architectures.
  • Sharing knowledge and best practices within the organization through mentoring, training, publication, and creating reusable artifacts.
  • Ensuring solutions meet industry standards and supporting customers in advancing their AI/ML, GenAI, and cloud adoption strategies.
  • 3+ years cloud architecture and implementation.
  • Bachelor's degree in Computer Science, Engineering, related field, or equivalent experience.
  • 5+ years data, software, or ML engineering, with strong understanding of distributed computing.
  • 3+ years developing predictive modeling, natural language processing, and deep learning, with a proven track record of building and deploying ML models on cloud.
  • 3+ years developing with SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, TypeScript). Proficient with leading ML libraries and frameworks (e.g., TensorFlow, PyTorch).
  • AWS experience preferred, with proficiency in a range of AWS services (e.g., SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, Step Functions, VPC, CloudFormation).
  • AWS Professional certifications (e.g., Solutions Architect Professional, DevOps Engineer Professional).
  • Experience with automation (e.g., Terraform, Python), Infrastructure as Code (e.g., CloudFormation, CDK), and Containers & CI/CD Pipelines.
  • Knowledge of common security and compliance standards (e.g., HIPAA, GDPR).
  • Strong communication skills with ability to explain complex concepts to technical and non-technical audiences.
  • 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).
  • Base pay for this position ranges from $118,200/year in our lowest geographic market up to $204,300/year in our highest geographic market.
  • Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package.
  • Full range of medical, financial, and/or other benefits.
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