Business Analyst — Fit/Gap & Process Analysis

Guidacent is seeking a Principal DevOps Engineer experienced in AI daily use to improve individual and team work efforts. This is a senior individual-contributor role with significant influence: the right candidate will architect the delivery platform and developer experience (DevEx), define standards and reusable "paved roads", make cross-team technical decisions, and mentor other engineers. We are looking for a strong DevOps practitioner first - someone deep in CI/CD, infrastructure-as-code, cloud, containers, and observability - who has folded AI tooling such as Claude or Cursor into their daily workflow and used it to make engineering standards and practices measurably better. Just as important, the right candidate knows how to be a careful, cost-aware user of these tools: getting real leverage from AI without token overuse. The role extends proven DevOps practice with AIOps and AI-assisted automation, and treats DevEx as a long-term, principal-owned concern. Formal people or team management is a welcome plus but not a requirement, but preference is for a hands-on principal-level IC and player-coach who wants to lead a team. Key Responsibilities Technical direction & architecture: Own the architecture for CI/CD, infrastructure, and the AI delivery platform; set the technical strategy and reference architectures that other teams build on. Developer experience (DevEx): Own developer experience as a long-term, principal-level charter - continuously improve how engineers build, test, and ship, and treat DevEx as a product with its own metrics and roadmap. Standards & paved roads: Define and champion reusable templates, golden paths, and engineering standards that make delivery fast, safe, and self-service - using AI tooling to raise the bar on those standards, not just to move faster. AI in the daily workflow: Set the example and the patterns for using AI coding tools (e.g., Claude, Cursor) day to day, and turn that hands-on use into improved practices, reviews, and automation across the team - with disciplined, cost-aware token usage rather than wasteful consumption. CI/CD delivery: Lead the design of continuous integration and deployment pipelines with automated testing, validation, and rollback. Infrastructure as code: Establish IaC and GitOps practices (Terraform or equivalent) across one or more major clouds. Scaling AI tooling & AIOps: Drive org-wide adoption of AI-assisted review, test generation, and AIOps-driven incident detection and remediation, and quantify their impact on quality, velocity, and reliability. Observability & AIOps: Define observability strategy - telemetry, logging, automated anomaly detection, and alerting - and apply AIOps techniques to set SLOs that reduce mean-time-to-resolution. DevSecOps & responsible AI use: Embed security scanning, secrets management, and sensible guardrails for how AI tooling is used (e.g., data handling, access, and review practices) across the delivery lifecycle. Reliability & cost: Set reliability practices (SLOs, DORA metrics) and optimize infrastructure and tooling cost - including efficient, token-aware use of AI tools. Mentorship & influence: Mentor and uplevel engineers, lead technical reviews, and influence the broader engineering roadmap and ways of working. Required Qualifications 8+ years in DevOps, platform engineering, or site reliability engineering, including a track record of senior technical ownership. Demonstrated experience architecting and setting direction for CI/CD and infrastructure platforms used across multiple teams. Strong CI/CD experience (e.g., GitHub Actions, GitLab CI, Jenkins, or Azure DevOps). Deep proficiency with infrastructure-as-code (Terraform, Pulumi, or CloudFormation) and at least one major cloud (AWS, Azure, or GCP). Hands-on containerization and orchestration with Docker and Kubernetes, including production-scale operations. Strong scripting and automation skills in Python, Bash, or Go. Experience defining observability and monitoring strategy (e.g., Prometheus, Grafana, Datadog, ELK). Regular, hands-on use of AI to make DevOps work more efficient - automating tasks, generating and reviewing code, troubleshooting, and improving tooling. Daily, hands-on use of AI coding tools (e.g., Claude, Cursor, or equivalent) as a core part of how they work - with a cost-conscious, token-aware approach rather than wasteful consumption. A demonstrated track record of using AI to make engineering standards and practices measurably better (e.g., better reviews, tests, automation, or paved-road tooling), not just to ship faster. Some level of AIOps experience - using AI-driven techniques for monitoring, anomaly detection, alerting, or automated remediation. Genuine interest in and ownership instinct for developer experience (DevEx) as a long-term concern. Proven ability to mentor engineers, lead technical decisions, and influence across teams. Preferred Qualifications People or team management experience - leading, growing, or formally managing a DevOps / platform / DevEx team. A strong plus for candidates on a player-coach or principal-with-reports path, but not required. Experience standing up or owning a platform or developer-experience function from the ground up. Experience evaluating, rolling out, and measuring AI developer tools (e.g., Claude, Cursor, Copilot) across a team or organization. Familiarity with AIOps platforms and automated remediation. Cost / FinOps discipline for cloud and AI tooling - monitoring and optimizing spend, including token usage. Awareness of responsible / secure AI-use practices and relevant data-handling requirements. Relevant certifications (CKA/CKAD, cloud architect or DevOps engineer, Terraform Associate). Exposure to enterprise ITSM/ITOM platforms (e.g., ServiceNow) and large-scale program environments. #J-18808-Ljbffr Guidacent