Lead DevOps Engineer/ Cloud Solution Architect (Google Cloud Platform)

Lead DevOps Engineer/ Cloud Solution Architect (Google Cloud Platform)

Raritan, NJ

Fulltime role

Experience Required

12+ Years

 

Job Summary

We are seeking a highly experienced Lead DevOps Engineer / Cloud Solution Architect with deep expertise in Google Cloud Platform (Google Cloud Platform) and modern cloud-native technologies. This is a hands-on technical leadership role that combines solution design with direct implementation. The individual will design secure, scalable, resilient, and cost-effective cloud solutions; build critical platform components and automation; and lead engineers through delivery and production support.

The role requires practical expertise in Google Cloud Platform networking, identity and access management, infrastructure as code, CI/CD, container orchestration, serverless technologies, observability, data services, and generative AI platforms. The candidate will translate business and nonfunctional requirements into implementable solution designs, validate decisions through prototypes and hands-on engineering, develop reusable patterns, review technical deliverables, and troubleshoot complex production issues. The role will partner with application, data, security, architecture, and operations teams to ensure solutions meet security, compliance, reliability, performance, operational, and cost objectives. Solid AWS experience is expected; Azure experience is preferred but not required.

Key Responsibilities

Solution Design & Technical Leadership

• Own end-to-end solution design for cloud infrastructure, DevOps, platform engineering, and application-enablement initiatives.
• Translate functional and nonfunctional requirements into architecture diagrams, detailed technical designs, implementation plans, and reusable engineering patterns.
• Evaluate design trade-offs across security, reliability, performance, scalability, operability, delivery effort, and cost.
• Remain hands-on by developing prototypes, Terraform modules, CI/CD pipelines, scripts, platform configurations, and reference implementations for complex or high-risk solutions.
• Lead technical reviews, guide engineers during implementation, perform code and configuration reviews, and resolve design or delivery blockers.
• Mentor engineers and promote practical standards, documentation, automation, and knowledge sharing across teams.
• Collaborate with application, data, AI/ML, cybersecurity, enterprise architecture, operations, and FinOps teams to deliver solutions from design through production support.

Google Cloud Platform Platform Engineering & Cloud Infrastructure

• Design, build, and support secure, highly available, resilient, and cost-optimized infrastructure on Google Cloud Platform.
• Implement and maintain multi-project Google Cloud Platform environments using organization policies, resource hierarchy, folder structures, Shared VPC, service projects, and standardized landing-zone patterns.
• Design and configure secure networking using Shared VPC, Private Service Connect (PSC), VPC peering, Serverless VPC Access connectors, Cloud DNS, Cloud Load Balancing, routing, and firewall policies.
• Develop and execute migration, modernization, business-continuity, and disaster-recovery solutions for cloud workloads.
• Apply least-privilege principles and cloud security best practices across platform implementations.

Identity & Security

• Design and implement identity, security, and compliance solutions in partnership with cybersecurity and risk teams.
• Configure Google Cloud Platform IAM, custom roles, service accounts, Workload Identity, Workload Identity Federation, OIDC, OAuth, and service-to-service authentication.
• Build practical guardrails and policy-as-code controls aligned with least privilege, auditability, and regulatory requirements.
• Implement secure secrets, key, and certificate-management solutions for enterprise workloads.

Kubernetes & Platform Operations

• Design, deploy, and operate production-grade Google Kubernetes Engine (GKE) clusters.
• Manage cluster upgrades, autoscaling, security hardening, observability, backup, and disaster recovery.
• Support shared platform services and developer self-service capabilities.
• Manage ingress, service mesh, networking, and workload security across Kubernetes environments.

Serverless & Cloud-Native Services

• Design and support cloud-native applications using Cloud Run, Cloud Functions, and event-driven architecture.
• Build scalable API and microservices platforms using managed Google Cloud Platform services.
• Integrate serverless workloads with enterprise networking and identity solutions.

Data & Analytics Platform Support

• Deploy and support cloud data platforms using Dataproc, Dataform, Cloud SQL, and BigQuery.
• Collaborate with data engineering teams to automate infrastructure provisioning and operational processes.
• Support data pipelines, platform observability, security, and performance optimization.

AI & Generative AI Platform Engineering

• Build and support enterprise AI and generative AI platforms on Google Cloud.
• Implement and integrate Gemini models, the Gemini Agent Platform, Vertex AI services, and agent-based architectures.
• Partner with application and data teams to operationalize generative AI workloads.
• Establish deployment, governance, monitoring, and security practices for AI solutions.

CI/CD & Automation

• Design, build, and maintain CI/CD pipelines using Jenkins.
• Automate infrastructure provisioning, application deployment, testing, security scanning, and rollback processes.
• Develop reusable automation and operational tooling with Python and Bash.
• Implement DevSecOps controls, including automated scanning, compliance checks, and policy enforcement.
• Provide hands-on support for pipeline failures, deployment issues, and release-critical changes.

Infrastructure as Code

• Develop, test, and maintain infrastructure using Terraform.
• Create reusable modules, environment configurations, and standardized deployment patterns.
• Review Terraform code, state-management approaches, and change plans for quality, security, and operational safety.
• Support GitOps workflows and automated release management.

Monitoring, Reliability & Operations

• Design and implement monitoring, logging, tracing, alerting, service-level objectives, and operational dashboards.
• Build and validate reliability, capacity, backup, disaster-recovery, and operational-readiness solutions for business-critical platforms.
• Lead and actively participate in troubleshooting, incident resolution, root-cause analysis, and remediation for complex production issues.
• Use operational, reliability, delivery, and cloud-cost metrics to identify risks and implement continuous improvements.

Required Qualifications

• 12 or more years of experience in DevOps, cloud engineering, platform engineering, site reliability engineering, or related disciplines, including substantial hands-on delivery and technical leadership experience.
• Proven ability to translate business, security, compliance, and nonfunctional requirements into practical solution designs and lead them through implementation and production support.
• Deep hands-on expertise with Google Cloud Platform, including enterprise landing zones, multi-project environments, hybrid or multi-cloud integrations, networking, and workload migrations.
• Deep expertise in Google Cloud Platform IAM, Workload Identity, Shared VPC, Private Service Connect, Serverless VPC Access connectors, GKE, Cloud Run, and Cloud Functions.
• Experience designing and implementing solutions with Dataproc, Dataform, Cloud SQL, and BigQuery.
• Advanced hands-on experience with Jenkins, GitHub, GitOps, Terraform, Docker, and Kubernetes.
• Strong Python and Bash scripting skills, with the ability to develop production-ready automation and troubleshooting utilities.
• Strong command of cloud security, networking, reliability, disaster recovery, observability, performance engineering, and cost optimization.
• Demonstrated ability to lead technical reviews, mentor engineers, influence stakeholders, and communicate complex decisions clearly to technical and nontechnical audiences.
• Willingness and ability to personally implement, test, troubleshoot, and support critical solutions rather than operating solely in an advisory or governance capacity.

Preferred Qualifications

• Experience with the Gemini Agent Platform, Vertex AI, generative AI solutions, AI/ML operationalization, and responsible AI governance.
• Hands-on AWS architecture experience with IAM, VPC, EKS, Lambda, CloudFormation, and Terraform.
• Experience applying the Google Cloud Well-Architected Framework and leading cloud architecture assessments.
• Google Cloud Professional Cloud Architect certification is strongly preferred; Professional Cloud DevOps Engineer, Cloud Security Engineer, or Cloud Network Engineer certifications are advantageous.
• Kubernetes certifications such as CKA, CKAD, or CKS are highly desirable.
• Azure architecture experience is beneficial but not required.