AWS Engineer

About the role

Are you a hands-on cloud engineer who thrives on building scalable, secure, cost-efficient AWS solutions?

As an AWS Cloud & MLOps Engineer at EXL, you'll design, develop, deploy, and maintain scalable cloud-based data and document-processing pipelines, and stand up the AWS ML platform (Bedrock, SageMaker, QuickSight) that runs our production models. You'll work closely with application, DevOps, data, and infrastructure teams to deliver highly available AWS solutions that power critical operations in the US healthcare payment space.

*Base Pay Range: $60,100 - $98,700

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits

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, deployed through Amazon ECR / ECS / AWS Batch.

Work with GPU and CPU-based workloads, including provisioning and scaling compute resources.

Stand up and operate the AWS ML platform using Amazon Bedrock, Amazon SageMaker, and Amazon QuickSight, the go-forward baseline for running all our models.

Develop automation using Python, Shell scripting, and AWS CLI / SDK (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 data stores used by processing pipelines.

Resolve high-priority production incidents with precision, drive zero-downtime operations, and proactively prevent recurrence.

You're not just running infrastructure. You're building the cloud and ML platform that powers payer operations, ensures compliance, and scales with the business.

Deliver AWS solutions that work at scale, with reliability, speed, security, and cost-efficiency built in.

Own production performance and observability across pipelines, compute, and data-ingestion points.

Collaborate with application, DevOps, data, and infrastructure teams to ship reliable, production-grade cloud services.

Apply strong architecture principles, automation, and agile delivery to accelerate time-to-market.

Proactively remove roadblocks, anticipate risks, and communicate mitigation plans clearly to stakeholders.

Collaborative Interactions:

Internal: Partner with data engineers, DevOps, infrastructure, application teams, and tech leads. Share knowledge and grow cloud depth across the team.

External: Engage with client-side stakeholders to understand expectations, demo capabilities, and bridge business needs with technical delivery.

Overall 4-6 years of demonstrable hands-on delivery of production AWS pipelines, infrastructure as code, and ML-platform enablement experience

AWS Batch, ECS, Lambda, Step Functions, SQS, SNS, S3, EventBridge

Containerization with Docker, deployed via Amazon ECR / ECS / AWS Batch

GPU & CPU workload provisioning and elastic scaling

Amazon Bedrock, Amazon SageMaker, Amazon QuickSight for model hosting, training, and analytics

Model deployment, endpoints, and serving as the go-forward baseline for running production models

Integration of GenAI/LLM and ML inference into data and document-processing pipelines

IAM, VPC, Security Groups, KMS, Load Balancers, S3 permissions

IaC with CloudFormation, AWS CDK, or Terraform

Cost-efficient, highly available, secure architecture by design

Python, Shell scripting, AWS CLI / Boto3 (SDK)

CI/CD with Azure DevOps, GitHub Actions, or AWS CodePipeline

Version control (Git / Azure DevOps), automated testing, release automation

RDS / PostgreSQL and other data stores powering processing pipelines

Query and performance tuning in high-volume environments

Logging, monitoring, alerting, and operational dashboards using CloudWatch and related AWS services

Nice to Have:

OCR & NLP-driven document processing for unstructured data

US Healthcare payer / Payment Integrity domain exposure

AWS certifications (Solutions Architect, DevOps Engineer, or ML Specialty); agile certifications

US Healthcare Insurance / Payer & Payment Integrity context (preferred)

Strong ownership, end-to-end accountability, and production-first mindset

Clear, consultative communication; collaborates across DevOps, data, and infrastructure teams

Educational Requirements:

Master's or bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or equivalent experience.

AWS certifications a strong plus; US Healthcare certifications welcome.

*The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

Posting text as published by EXL.

The evidence behind this listing

Workplace

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Seniority

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Employment type

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Location as listed

United States

Pay and work authorization

Stated pay

$60.1k–98.7k/yr AI reading of the posting

Visa sponsorship

Not stated

Security clearance

Not established

Citizenship

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