DevOps Engineer

Consensus

10d•$130,000 - $140,000•Remote

About The Position

We're seeking an exceptional Senior DevOps Engineer with deep CI/CD expertise and proven AI tool proficiency who thrives at the intersection of automation excellence and cutting-edge technology. If you're passionate about building world-class deployment pipelines, leveraging AI to supercharge productivity, and solving complex technical challenges with modern tools, this role is for you. You'll be the driving force behind our continuous integration and deployment strategy, architecting sophisticated pipelines that enable our development teams to ship features rapidly and reliably. Your ability to harness AI-powered tools like Claude Code, Cursor, and intelligent automation platforms will be critical to accelerating our engineering velocity while maintaining the highest standards of quality and security. This role will help Consensus meet aggressive scale and platform growth challenges using the best technologies available, with AI tools as a core part of your daily workflow.

Requirements

4+ years of DevOps experience with strong CI/CD focus
Expert-level experience designing and implementing complex CI/CD pipelines using Jenkins, GitLab CI, GitHub Actions, or similar
Advanced knowledge of GitOps principles and tools like ArgoCD, Flux, or Spinnaker
Strong understanding of build optimization, artifact management, and deployment strategies
Experience with progressive delivery patterns: canary deployments, blue-green releases, feature flags
Proficiency with continuous testing strategies including automated testing frameworks, contract testing, and test environment management
Deep Git expertise including branching strategies, pull request workflows, and trunk-based development
Proven track record using AI-powered development tools (Claude Code, GitHub Copilot, Cursor, Amazon CodeWhisperer, or similar) in production environments
Strong prompt engineering skills—ability to effectively collaborate with AI assistants for complex infrastructure problems
Demonstrated efficiency gains from AI tool adoption in previous roles
Understanding of responsible AI practices, including security considerations and code validation when using AI assistants
Experience with AI-enhanced testing, security scanning, or deployment automation
2+ years hands-on experience with AWS (EC2, ECS/EKS, Lambda, S3, RDS, CloudFormation)
Expert-level Terraform skills for infrastructure-as-code
Production experience with Docker and Kubernetes including multi-cluster management
Strong automation background using Ansible, Chef, Salt, or similar tools
Advanced Python programming skills for automation, tooling, and system integration
Expert-level shell scripting (Bash, sh) for operational automation and CI/CD scripting
Solid understanding of platform, application, and data security including SOC2 compliance requirements
Linux/Unix systems expertise with strong command-line proficiency and system administration skills
Database knowledge across relational, NoSQL, and distributed data stores

Nice To Haves

Experience with AIOps platforms (Datadog AI, New Relic AI, Dynatrace AI, Moogsoft)
MLOps knowledge or experience supporting AI/ML model deployment pipelines
Familiarity with artifact repositories like JFrog Artifactory, Nexus, or AWS ECR
Background in cloud cost optimization using FinOps practices and AI-powered tools
Experience with service mesh technologies (Istio, Linkerd) for advanced traffic management
Observability platform expertise (Prometheus, Grafana, ELK stack, Datadog)
Multi-cloud experience (Aws, Azure, GCP, OCI)

Responsibilities

Design and build sophisticated CI/CD pipelines that support rapid feature deployment across microservices and distributed systems
Leverage AI code generation tools (Claude Code, Cursor, GitHub Copilot) to accelerate pipeline development, reduce boilerplate, and implement intelligent testing strategies
Write custom pipeline scripts and integrations using Python and shell scripting to extend and enhance CI/CD capabilities
Architect GitOps workflows using modern tools like ArgoCD, Flux, or similar platforms
Implement progressive delivery strategies including blue-green deployments, canary releases, and feature flags
Create intelligent build optimization using AI-powered caching, parallel execution, and dependency analysis
Champion AI tool adoption across the DevOps lifecycle—from infrastructure-as-code to incident response
Use AI pair programming tools daily to write Terraform modules, Python automation scripts, shell scripts, Kubernetes manifests, and troubleshooting workflows
Implement AIOps practices for self-healing systems, intelligent alerting, anomaly detection, and predictive maintenance
Leverage AI-driven monitoring and observability to proactively identify issues before they impact customers
Employ AI-powered log analysis and root cause analysis to dramatically reduce MTTR (Mean Time To Resolution)
Build developer-friendly platform tools that abstract infrastructure complexity and accelerate feature delivery
Automate everything: deployments, infrastructure provisioning, security scanning, compliance checks, and operational runbooks using Python and shell scripting
Develop custom automation scripts and CLI tools in Python to streamline operations and integrate disparate systems
Design container orchestration strategies using Kubernetes with AI-enhanced optimization and scaling
Implement AI-powered capacity planning and cost optimization to drive data-informed infrastructure decisions
Integrate intelligent security scanning and automated vulnerability remediation into CI/CD workflows
Partner with engineering teams to optimize their delivery pipelines and remove bottlenecks
Evaluate and adopt emerging AI technologies that improve team efficiency and system reliability
Create comprehensive documentation using AI assistants to accelerate knowledge sharing
Drive DevOps culture through mentorship, best practices evangelism, and technical leadership
Implement feedback loops from production monitoring into development practices