DevOps Engineer

Position Summary

Dealer Information Systems Corporation (DIS) is seeking an experienced AI DevOps Engineer to design, automate, secure, and optimize cloud-native infrastructure on AWS while leveraging AI-powered tools and practices to accelerate software delivery, improve operational efficiency, and enhance platform reliability. The ideal candidate combines strong AWS DevOps expertise with practical experience integrating AI technologies into CI/CD pipelines, cloud operations, observability, security, and infrastructure automation.

This role will work closely with Software Engineering, Cloud Architecture, Security, Data, and AI teams to build scalable, resilient, and intelligent cloud platforms that support modern application development and AI-driven workloads.

Division Link:

Key Responsibilities:

AWS Cloud Infrastructure & Platform Engineering

• Design, implement, and manage highly available AWS cloud environments.

• Build and maintain infrastructure using Infrastructure as Code (IaC) tools such as Terraform and AWS CloudFormation.

• Administer and optimize AWS services including:

• ECS / EKS

• EC2

• Lambda

• RDS

• DynamoDB

• S3

• Route53

• CloudFront

• API Gateway

• IAM

• VPC

• AWS Organizations

• Implement scalable multi-account and multi-environment cloud architectures.

DevOps & CI/CD Automation

• Design and maintain CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or similar platforms.

• Automate application deployments across development, QA, staging, and production environments.

• Implement GitOps practices and deployment strategies including blue-green, canary, and rolling deployments.

• Improve deployment frequency, reliability, and release quality through automation.

AI-Driven DevOps Enablement

• Integrate AI-powered development and operational tools into engineering workflows.

• Leverage Generative AI platforms such as:

• OpenAI

• Amazon Bedrock

• Anthropic Claude

• GitHub Copilot

• Amazon Q

• Cursor

• Develop AI-assisted solutions for:

• Infrastructure automation

• Incident analysis

• Root cause identification

• Log analysis

• Automated remediation

• Capacity forecasting

• Deployment validation

• Cost optimization

• Build internal AI agents and automation workflows to streamline operational processes.

Platform Reliability & Observability

• Implement monitoring, logging, and alerting solutions.

• Manage observability platforms such as:

• CloudWatch

• Grafana

• Prometheus

• Datadog

• ELK / OpenSearch

• New Relic

• Define and maintain SLI, SLO, and SLA metrics.

• Conduct performance tuning and proactive reliability improvements.

DevSecOps & Security

• Embed security controls throughout the software development lifecycle.

• Implement automated security scanning and compliance checks.

• Utilize tools such as:

• Trivy

• SonarQube

• Snyk

• Dependabot

• Checkov

• AWS Security Hub

• GuardDuty

• Support cloud governance, vulnerability management, and compliance initiatives.

AI/ML Infrastructure Support

• Provision and manage infrastructure supporting AI/ML workloads.

• Deploy and optimize:

• Amazon SageMaker

• Bedrock-based applications

• Vector databases

• GPU-enabled workloads

• AI inference services

• Support MLOps pipelines and model deployment automation.

Cloud Cost Optimization (FinOps)

• Monitor and optimize AWS cloud spend.

• Implement intelligent cost-monitoring and AI-driven recommendations.

• Improve resource utilization through automation and predictive scaling.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, or related field.

• 4+ years of AWS DevOps experience.

• 3+ years of experience managing containerized environments using ECS, EKS, Kubernetes, and Docker.

• Strong expertise in Infrastructure as Code using Terraform and/or CloudFormation.

• Experience building CI/CD pipelines using GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.

• Strong scripting skills in Python, Bash, or PowerShell.

• Experience with Linux administration and cloud networking.

• Hands-on experience with monitoring and observability platforms.

• Familiarity with Generative AI tools and AI-assisted software engineering practices.

• Strong understanding of cloud security, IAM, networking, and DevSecOps principles.

Preferred Qualifications

• Experience with Amazon Bedrock, Amazon Q, SageMaker, or other AI/ML platforms.

• Experience building AI-powered operational automation solutions.

• Knowledge of MLOps practices and model deployment workflows.

• AWS Solutions Architect, DevOps Engineer Professional, or Security Specialty certifications.

• Experience with large-scale SaaS platforms and multi-tenant environments.

• Exposure to Agentic AI frameworks, MCP servers, LangChain, LangGraph, CrewAI, AutoGen, or similar technologies.