Position: ML Ops Engineer Location: Atlanta, GA (onsite all 5 days) Final In person interview is mandatory at any of Hexaware Location (Plano TX, Chicago IL, Atlanta GA, Jersey City NJ and Herdon VA)
Job Description
The Analytics CoE is seeking an experienced IT Engineer to provide hands-on engineering capacity across enterprise Analytics, Data Science, and Machine Learning platforms.
The role
will focus on cloud platform engineering, automation, technical modernization, and engineering improvements across AWS-based analytics environments, with a primary emphasis on AWS SageMaker / Data Science capabilities. The engineer will design and implement solutions that improve platform scalability, resiliency, security, developer productivity, and operational efficiency. This individual will work closely with Analytics CoE engineers and partner technology teams to deliver platform enhancements, automate repeatable processes, resolve complex technical issues, strengthen engineering standards, and accelerate modernization initiatives.
Key Responsibilities
Design and implement enhancements for AWS-based Analytics and Data Science platforms, with a strong focus on SageMaker.
Develop automation for platform provisioning, administration, maintenance, and recurring engineering activities.
Improve platform scalability, resiliency, observability, security, and performance.
Perform complex troubleshooting and root-cause analysis and implement durable engineering solutions.
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Develop and enhance infrastructure-as-code and CI/CD capabilities, including Terraform and GitLab-based deployment patterns where applicable.
Improve platform access, identity, integration, and self-service capabilities.
Address security and vulnerability remediation through automation and platform engineering.
Identify and implement cloud cost optimization / FinOps and resource-efficiency improvements.
Contribute to Domino platform engineering and modernization activities as needed.
Support remaining SAS Grid Exit engineering activities, including migration enablement, issue resolution, asset offboarding, and decommissioning.
Develop reusable engineering patterns, technical documentation, and runbooks.
Participate in knowledge transfer for key Domino, VTM, cost optimization, and platform-engineering
responsibilities
prior to Balaji’s departure. • Collaborate with Cloud Engineering, Information Security, DevOps, application teams, vendors, and other technology stakeholders.
Required Skills
/ Experience • Experience in cloud engineering, platform engineering, DevOps, infrastructure engineering, or related technology roles. • Strong hands-on experience with AWS services and cloud-native engineering. • Experience with AWS SageMaker or comparable enterprise Data Science / ML platforms. • Strong automation and scripting experience using Python, shell, or similar technologies. • Experience with Terraform, infrastructure-as-code, and CI/CD engineering. • Strong troubleshooting, systems analysis, and root-cause-analysis capabilities. • Experience engineering secure cloud environments, including identity/access controls and vulnerability remediation. • Experience with monitoring, observability, resiliency, and platform-performance engineering. • Experience with cloud cost optimization and resource-efficiency improvements. • Ability to design reusable solutions that reduce manual operational processes. • Strong communication, technical documentation, and cross-team collaboration skills. Preferred Skills • Experience with Domino Data Lab or comparable enterprise Analytics / ML platforms. • Experience with Python-based Data Science and ML workloads. • Experience with GitLab pipelines and automated platform deployment. • Experience integrating developer-productivity or AI-assisted development capabilities into cloud platforms. • Experience with AWS cost management / FinOps practices. • Experience with analytics-platform migrations, lifecycle modernization, and decommissioning. • Familiarity with vulnerability-management and compliance engineering. • SAS experience is beneficial for the remaining SAS Grid Exit closeout but is not a primary requirement. • Expected Assignment Outcomes