DevOps Engineer

Consensus is a SaaS company creating a continuous B2B buying experience through interactive product demos, AI demo agents, and live demos. The DevOps Engineer will lead CI/CD strategy, build sophisticated deployment pipelines, leverage AI-powered development and operations tools, and automate platform, infrastructure, security, and observability workflows.

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

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
• 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)
• 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