DevOps Architect

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Lead end-to-end architecture of scalable, secure, high-performing DevOps solutions supporting enterprise-grade deployment pipelines.
Define and own the DevOps reference architecture — establishing standards, patterns, and guardrails adopted across the organization.
Evaluate emerging technologies and recommend toolchain investments; design multi-cloud and hybrid infrastructure strategies.

Develop, evaluate, select, and integrate best-in-class tools across CI/CD, IaC, container orchestration, monitoring, and security.
Drive automation of infrastructure provisioning, deployment, and testing pipelines; champion IaC practices using Terraform, Ansible, and similar tools.
Build and maintain robust CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI) enabling rapid, reliable delivery across environments.
Champion adoption of AI-powered DevOps tooling — from AI-assisted code review and intelligent test generation to AIOps-driven incident detection — continuously identifying where AI can reduce toil and accelerate delivery.

Mentor and coach engineers on DevOps methodologies, cloud-native patterns, and platform engineering best practices.
Conduct architecture reviews, set a high technical bar, and collaborate with engineering managers and product leadership to align DevOps strategy with business goals.
Contribute to hiring and technical interviewing to grow the DevOps and platform engineering function.

Design comprehensive observability solutions — monitoring, distributed tracing, centralized logging, and alerting — to ensure platform health and rapid incident response.
Define and enforce SLOs, SLIs, and error budgets; integrate DevSecOps practices and ensure compliance with SOC 2, ISO 27001, and relevant regulatory frameworks.
Lead vulnerability assessments and implement secrets management strategies across cloud and on-premises infrastructure.

Provide expert-level support for complex infrastructure and SaaS platform issues, including root cause analysis and preventive remediation.
Participate in on-call rotations for 24/7 incident response; collaborate effectively with globally distributed teams across time zones.

10+ years in DevOps, platform engineering, infrastructure, or SRE roles — with at least 3 years in a senior architect or technical lead capacity.
Demonstrated ability to design and deliver enterprise-scale DevOps solutions in complex, multi-team environments, influencing decisions across cross-functional teams.
Background supporting SaaS applications in production, including incident management, performance tuning, and continuous improvement.

Bachelor’s Degree in Computer Science, Software Engineering, IT, or related field required; Master’s preferred.
Advanced certification in AWS, Azure, or GCP (e.g., Solutions Architect Professional, DevOps Engineer Expert, Professional Cloud Architect).
Certifications in Kubernetes (CKA/CKAD), HashiCorp Terraform, or security frameworks (CISSP, CompTIA Security+) are a plus.

Deep expertise in at least one major cloud platform (AWS, Azure, or GCP) with working knowledge of multi-cloud architectures.
Strong proficiency with Docker, Kubernetes, and equivalent orchestration platforms (ECS/EKS, AKS, GKE); hands-on experience with Terraform, Pulumi, Ansible, or CloudFormation.
Solid command of CI/CD tooling (Jenkins, GitHub Actions, GitLab CI/CD, CircleCI) and scripting languages (Python, Bash, Go, or Ruby).
Experience administering MS SQL Server and MongoDB; working knowledge of Linux and Windows administration, networking fundamentals, and observability platforms (Datadog, Prometheus/Grafana, Splunk, ELK).

Practical understanding of AI/ML as applied to DevOps — including AI-assisted coding tools (GitHub Copilot, Amazon CodeWhisperer), AIOps platforms for anomaly detection, and LLM-powered automation for incident triage and runbook generation.
Experience evaluating and integrating AI-powered DevOps tooling (predictive scaling, intelligent test selection, automated security scanning) with clear-eyed assessment of tradeoffs in accuracy, cost, and reliability.
Familiarity with infrastructure patterns supporting AI/ML workloads: GPU compute, model serving, vector databases, and ML pipeline orchestration (Kubeflow, MLflow, SageMaker Pipelines).
Proactive mindset toward emerging technology — translating advancements in AI, platform engineering, and cloud-native ecosystems into actionable improvements for the team.

Exceptional communicator — able to translate complex technical concepts for both engineering peers and executive stakeholders.
Strong mentorship instincts with a passion for growing engineers and fostering a culture of continuous learning and operational excellence.
Proven ability to manage multiple high-priority workstreams; comfortable in a globally distributed environment across time zones.

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