AWS Engineer

Vaga não está no LinkedIn

🕒 Agosto 27

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $60.100 - $98.700 / ano

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

☁️ Engenheiro de Cloud

👻 Score fantasma 13%

O que significa: a chance de ninguém estar realmente contratando para esta vaga.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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EXL

SiteLinkedInTodas as Vagas

10.000+ funcionários

🏥 Saúde

🛡️ Seguros

📦 Logística

💰 $2.000.000 Venture Round em 2015-01

Healthcare • Insurance • Logistics

Escolher um parceiro digital vai além das capacidades — trata-se de colaboração e caráter.

Descrição

• Design and implement scalable AWS data and document-processing pipelines • Develop and maintain pipelines using AWS Batch, ECS, Lambda, Step Functions, SQS, SNS, S3, and EventBridge • Build containerized workloads with Docker through Amazon ECR, ECS, and AWS Batch • Provision and scale GPU- and CPU-based workloads • Stand up and operate the AWS ML platform using Amazon Bedrock, SageMaker, and QuickSight • Develop automation using Python, Shell scripting, AWS CLI, and Boto3 • Implement CI/CD pipelines using Azure DevOps, GitHub Actions, or AWS CodePipeline • Manage infrastructure as code using CloudFormation, AWS CDK, or Terraform • Configure and troubleshoot IAM roles, security groups, VPC, load balancers, KMS, and S3 permissions • Implement logging, monitoring, alerting, and operational dashboards using CloudWatch and related AWS services • Work with RDS/PostgreSQL and other processing-pipeline data stores • Resolve high-priority production incidents, drive zero-downtime operations, and prevent recurrence • Deliver reliable, secure, scalable, and cost-efficient AWS solutions • Own production performance and observability across pipelines, compute, and data-ingestion points • Collaborate with application, DevOps, data, and infrastructure teams • Engage client-side stakeholders, demonstrate capabilities, and bridge business needs with technical delivery

🎯 Requisitos

• Overall 4-6 years of demonstrable hands-on delivery of production AWS pipelines, infrastructure as code, and ML-platform enablement experience • Experience with AWS Batch, ECS, Lambda, Step Functions, SQS, SNS, S3, and EventBridge • Containerization with Docker, deployed via Amazon ECR, ECS, or AWS Batch • GPU and CPU workload provisioning and elastic scaling • Experience with Amazon Bedrock, Amazon SageMaker, and Amazon QuickSight for model hosting, training, and analytics • Model deployment, endpoints, and serving for production models • Integration of GenAI/LLM and ML inference into data and document-processing pipelines • Knowledge of IAM, VPC, Security Groups, KMS, Load Balancers, and S3 permissions • Infrastructure as code with CloudFormation, AWS CDK, or Terraform • Python, Shell scripting, AWS CLI, and Boto3 SDK • CI/CD with Azure DevOps, GitHub Actions, or AWS CodePipeline • Version control with Git or Azure DevOps, automated testing, and release automation • RDS/PostgreSQL and other data stores • Query and performance tuning in high-volume environments • Logging, monitoring, alerting, and operational dashboards using CloudWatch and related AWS services • Master's or bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or equivalent experience • AWS certifications are a strong plus; US Healthcare certifications welcome • OCR and NLP-driven document processing, US healthcare payer/payment integrity exposure, and agile certifications are nice to have

🏖️ Benefícios

• Benefits information provided at https://www.exlservice.com/us-careers-and-benefits • Potential compensation above the posted range for higher-cost zones such as California, New York, and New Jersey • Base salary determined by skills and experience, internal pay equity, work location, market conditions, and role scope

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