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AWS Engineer

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $60.1k - $98.7k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

☁️ Cloud Engineer

👻 Ghost score 2%

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Logo of EXL

EXL

10,000+ employees

🏥 Healthcare

🛡️ Insurance

📦 Logistics

💰 $2M Venture Round on 2015-01

Healthcare • Insurance • Logistics

EXL is a business consulting and services firm that focuses on leveraging data to enhance business operations and decision-making. With a strong emphasis on collaboration and adaptability, EXL partners with organizations to address their unique needs and culture while integrating data science and technology solutions. The company's areas of expertise include operations management, decision analytics, digital transformation, and various industries such as healthcare, finance, and insurance. EXL's mission is to help clients drive business evolution and maintain competitive advantage through tailored solutions and effective use of data.

📋 Description

• 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

🎯 Requirements

• 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

🏖️ Benefits

• 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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