GenAI & ML Delivery Architect

Amazon

Herndon (VA)

On-site

USD 131,000 - 178,000

Full time

12 days ago
Application generator

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Benefits offered by this job

Health insurance
401(k) matching
Parental leave
Paid time off

Job summary

Amazon Web Services (AWS) Professional Services seeks an experienced ML Engineer to join our Delivery Consultant team. You will design, implement, and manage AI/ML and GenAI solutions for enterprise customers, guiding them through cloud adoption and best practices across the ML project lifecycle.

As a trusted advisor, you will collaborate with Applied Science, DevOps, Data Engineering, and Cloud Infrastructure teams to architect scalable ML pipelines, ensure secure deployments, and enable rapid

Qualifications

  • 3+ years of software development experience.
  • 3+ years of building ML and GenAI models for business use.
  • Experience with cloud services related to ML (SageMaker) and GenAI applications.

Responsibilities

  • Implement end-to-end AI/ML and GenAI projects from understanding needs to deployment and monitoring.
  • Design scalable ML pipelines for high-performance, reliable workloads.
  • Develop MLOps-enabled solutions using AWS services and GenAI where applicable.
  • Collaborate with Applied Science, DevOps, Data Engineering, Cloud Infra, Applications teams to operationalize data and models.
  • Serve as trusted advisor for AI/ML and cloud architectures.
  • Share knowledge through mentoring, training, and reusable artifacts.
  • Ensure solutions meet industry standards and help customers advance AI/GenAI adoption.

Skills

Software development
ML/GenAI experience
Cloud ML services
Cross-functional collaboration
Architecting ML pipelines

Tools

SageMaker
Bedrock
EC2
ECS
EKS
OpenSearch
LangChain
LangGraph
LlamaIndex
PyTorch
TensorFlow

Job description

Amazon Web Services (AWS) Professional Services seeks an experienced ML Engineer to join our Delivery Consultant team. You will design, implement, and manage AI/ML and GenAI solutions for enterprise customers, guiding them through cloud adoption and best practices across the ML project lifecycle.

As a trusted advisor, you will collaborate with Applied Science, DevOps, Data Engineering, and Cloud Infrastructure teams to architect scalable ML pipelines, ensure secure deployments, and enable rapid

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