DevOps Engineer, Consensus

• 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
• Collaborative and empathetic with a passion for enabling other engineers to succeed
• Self-motivated with excellent problem-solving abilities and a bias for action
• Quality-focused but pragmatic about balancing perfection with shipping value
• AI-forward and efficiency-obsessed—constantly seeking ways to leverage intelligent tools to eliminate toil
• A clear communicator who can explain complex technical concepts to diverse audiences
• A master pipeline architect with an eye for elegant automation and developer experience
• Relentlessly curious about emerging technologies and industry best practices
• Data-driven with strong judgment on when to leverage AI tools versus traditional approaches

Desirable

• Engineering at Consensus is where collaboration meets innovation. We operate with transparency and foster a blameless culture—mistakes aren’t something to hide; they’re fuel for growth. Solving real customer challenges is at the heart of everything we do, and we believe the best software comes from engineers who think like product owners. Here, your ideas matter, your work drives impact, and every line of code makes a difference
• We're seeking an exceptional 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
• 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