AWS Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design and develop scalable data and document-processing pipelines in AWS.

• Create and sustain pipelines utilizing AWS Batch, ECS, Lambda, Step Functions, SQS, SNS, S3, and EventBridge.

• Construct containerized workloads using Docker through Amazon ECR, ECS, and AWS Batch.

• Provision and scale workloads based on GPU and CPU resources.

• Establish and manage the AWS ML platform employing Amazon Bedrock, SageMaker, and QuickSight.

• Develop automation scripts using Python, Shell scripting, AWS CLI, and Boto3.

• Implement CI/CD pipelines utilizing Azure DevOps, GitHub Actions, or AWS CodePipeline.

• Manage infrastructure as code through 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 other related AWS services.

• Collaborate with RDS/PostgreSQL and various data storage solutions within processing pipelines.

• Address high-priority production incidents, ensuring zero-downtime operations and preventing future occurrences.

• Deliver dependable, secure, scalable, and cost-effective AWS solutions.

• Take ownership of production performance and observability across pipelines, compute resources, and data ingestion points.

• Work alongside application, DevOps, data, and infrastructure teams.

• Engage with client-side stakeholders, showcase capabilities, and align business needs with technical delivery.

⛳️ Requirements

• 4-6 years of proven hands-on experience in delivering production AWS pipelines, infrastructure as code, and enabling ML platforms.

• Proficient with AWS Batch, ECS, Lambda, Step Functions, SQS, SNS, S3, and EventBridge.

• Expertise in containerization using Docker, deployed via Amazon ECR, ECS, or AWS Batch.

• Experience in provisioning GPU and CPU workloads with elastic scaling capabilities.

• Familiarity with Amazon Bedrock, Amazon SageMaker, and Amazon QuickSight for model hosting, training, and analytics.

• Knowledge in model deployment, endpoints, and serving production models.

• Integration of GenAI/LLM and ML inference into data and document-processing workflows.

• Understanding of IAM, VPC, Security Groups, KMS, Load Balancers, and S3 permissions.

• Experience with infrastructure as code using CloudFormation, AWS CDK, or Terraform.

• Proficiency in Python, Shell scripting, AWS CLI, and Boto3 SDK.

• Familiarity with CI/CD processes utilizing Azure DevOps, GitHub Actions, or AWS CodePipeline.

• Version control experience with Git or Azure DevOps, along with automated testing and release automation.

• Experience with RDS/PostgreSQL and other data storage solutions.

• Strong skills in query and performance tuning in high-volume environments.

• Proficient in logging, monitoring, alerting, and creating 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 highly regarded; US Healthcare certifications are welcome.

• Familiarity with OCR and NLP-driven document processing, exposure to US healthcare payer/payment integrity, and agile certifications are additional advantages.

🏝️ Benefits

• Benefits information provided at https://www.exlservice.com/us-careers-and-benefits.

• Potential compensation exceeding the posted range in higher-cost areas 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.