Cloud & ML Infra Engineer for LLM Platform (AWS)

Engg

San Carlos

Híbrido

ARS 257.209.000 - 317.729.000

Jornada completa

14 días+
Generador de candidaturas

Consigue una respuesta de este empleador — un currículum y una carta de presentación adaptados exactamente a lo que busca la empresa.

Supera los filtros ATS

Ventajas ofrecidas por este puesto de trabajo

Healthcare coverage
3 weeks PTO
401(k)

Descripción de la vacante

Beacon AI is seeking Cloud and ML Infrastructure Engineers to lead the AWS foundation and LLM platform. You will design, implement, and operate scalable, reliable, and secure services, with focus areas in LLM infra and IoT infra.

Hands-on role, operating with first-principles thinking in a fast-paced environment. The role partners with engineers and product teams to deliver retrieval-augmented workflows, model endpoints, and data pipelines while optimizing cost and performance.

Formación

  • Experience building scalable AWS infrastructure for ML/AI workloads.
  • Knowledge of building secure, auditable cloud runtimes and data pipelines.
  • Experience with LangChain or similar tooling for prompt execution.

Responsabilidades

  • Design, provision, and maintain AWS infrastructure with IaC.
  • Build CI/CD and testing for apps, infra, and ML pipelines.
  • Operate secure networking, IAM, KMS, Secrets Manager, and logging.

Conocimientos

AWS
IaC (CDK/Terraform)
CI/CD
Security and IAM
LLM infra
LangChain integration
Vector databases
OpenSearch/ pgvector
Glue/Athena
Edge/ IoT infra

Herramientas

AWS CDK
Terraform
GitHub Actions
CodeBuild
CodePipeline
Bedrock/SageMaker
ECS/EKS
S3/Glue
Aurora PostgreSQL with pgvector
OpenSearch Serverless

Descripción del empleo

Beacon AI is seeking Cloud and ML Infrastructure Engineers to lead the AWS foundation and LLM platform. You will design, implement, and operate scalable, reliable, and secure services, with focus areas in LLM infra and IoT infra.

Hands-on role, operating with first-principles thinking in a fast-paced environment. The role partners with engineers and product teams to deliver retrieval-augmented workflows, model endpoints, and data pipelines while optimizing cost and performance.

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