Customer Engineer, Advanced Customer Engineering, Global Financial Services

Amazon Amazon · Big Tech · NY +1 · Solutions Architect

Customer Engineer focused on building Agentic AI and multi-modal Generative AI applications and workflows on AWS for financial services customers. This role involves creating end-to-end solutions, prototypes, and early product iterations, with a strong emphasis on hands-on development and customer collaboration.

What you'd actually do

  1. Lead trusted relationships with AWS customers' technology leadership, enterprise architects, solution architects, software architects, and software engineers to understand the customer’s technology challenges; dive deep and understand stated, unstated business needs, and challenges.
  2. Working backwards from AWS customer's business needs, create end-to-end solutions, prototypes, and first iteration of most lovable product (MLP) addressing technology challenges but also increasing business impact, innovation and agility.
  3. Educate customers of all sizes on the value proposition of AWS, and participate in deep architectural discussions and ensure solutions are designed for successful deployment in the cloud.
  4. Work with AWS customers to convey cloud computing security and risk management best practices.
  5. Work with other teams within AWS to drive customer success.

Skills

Required

  • Application development in the Cloud
  • Serverless and Managed services
  • AWS services (Amazon Bedrock, Amazon Bedrock AgentCore, SageMaker AI, SageMaker Unified Studio, Open Table Format/Apache Iceberg, Amazon S3 Tables, AWS Glue, AWS Lambda, Elastic Load Balancers, Amazon EC2/ECS/EKS, Amazon Fargate, Amazon RDS, NoSQL databases like Amazon DynamoDB, Amazon API Gateway, Amazon Cognito)
  • Developing client applications with different user interfaces and experiences (Web Front-Ends, Mobile Applications or Alexa Skills)
  • Hands-on experience with AI-driven software development practices
  • Generative AI technologies
  • Agentic AI applications and workflows
  • Multi-modal Generative AI applications

Nice to have

  • Cloud architectures and platforms
  • Modern data & analytics applications including data processing pipelines
  • Customer engagement and technical feasibility validation

What the JD emphasized

  • strong focus on building Agentic AI applications and workflows
  • multi-modal Generative AI applications
  • strong preference for hands-on experience with AI-driven software development practices
  • generative AI technologies

Other signals

  • Hands-on experience with AI-driven software development practices
  • Generative AI technologies
  • Agentic AI applications and workflows
  • Multi-modal Generative AI applications