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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Manager, Cloud Engineering - **Company:** Promevo, LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, BigQuery, Cloud Computing, Cloud Engineering, Software Quality, Continuous Integration, Information Engineering, DevOps, Human Resources Information System (HRIS), Data Flow Control, High-Level Architecture, Performance Tuning, Systems Development Life Cycle, Software Engineering, Google Cloud, Feature Engineering, Large Language Models, Prompt Engineering, Kubernetes, Information Technology, Deployment Automation, Data Analytics, Performance Monitor, Machine Learning Operations, Terraform, Serverless Computing, Microservices - **Published:** July 26, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=6c4de5a5a135e77f ## About the Role * Bachelor's degree in Computer Science, Engineering, or a related technical field. * 12+ years of experience in IT infrastructure, cloud architecture, data science, or software engineering, with at least 4+ years directly managing engineering teams (Architects, Developers, or Cloud Engineers). * Proven track record of managing and earning the respect of senior/principal-level technical talent across both infrastructure and software application development (FE/BE). * Deep expertise in Google Cloud Platform (GCP) technologies is required, with strong literacy in modern AI/ML infrastructure components and deployment strategies. * Strong understanding of modern architectural frameworks, including microservices, Kubernetes (GKE), serverless computing, and Infrastructure as Code (Terraform). * Exceptional communication skills, with a proven ability to translate highly complex technical concepts into clear business objectives for clients and executive stakeholders. * Experience balancing billable delivery utilization targets alongside team management responsibilities within a professional services context. * Active Google Certified Professional Cloud Architect (PCA) or equivalent expert-level cloud engineering certifications are highly preferred. * Experience leading internal upskilling initiatives, specifically around Gen AI and AI-driven automation frameworks. * Familiarity with modern HRIS and performance tools (e.g., Lattice, Paycor) to drive data-informed talent decisions and continuous feedback cycles. ## Description Team Leadership * Lead, mentor, and manage a high-performing team of technical professionals, including Principal Cloud Architects, Systems Architects, Software Engineers (FE/BE), and Cloud Engineers, fostering a culture of technical excellence. * Foster a culture of continuous development by conducting semi-annual individual development discussions and establishing quarterly learning objectives centered on AI-upskilling and Gen AI literacy. Strategic Delivery * Serve in a 'player-coach' capacity, balancing team leadership with hands-on technical contribution. You will meet a scaled, manager-level billable delivery target, focusing on high-level architecture reviews, complex scoping, and strategic technical advisory, while ensuring your team remains optimally deployed. * Helps support utilization targets for themselves and team members by driving high-value, high-impact technical discoveries; leverages delivery time to uncover expansion or upsell opportunities within enterprise accounts; serves as a recognized industry expert on the Google Cloud ecosystem. Technical Governance * Guide the team in designing and implementing modern AI infrastructure pipelines, including LLM integration patterns, data orchestration layers, and secure enterprise AI frameworks. * Pioneers the creation of repeatable intellectual property, automated accelerators, or code repositories that reduce delivery time for the entire engineering organization; positions Promevo at the forefront of Google Cloud innovation through cutting-edge practice development. * Oversee engineering resource allocation and capacity planning, partnering closely with the PMO to ensure the right technical talent is assigned to the right projects at the right time. * Govern and elevate engineering standards, DevOps methodologies, code quality, and architectural review processes across full-stack application and cloud infrastructure delivery. * AI First/Augmented SDLC - Guides teams to build intelligent, AI-driven Google Cloud infrastructure (utilizing Vertex AI, advanced MLOps pipelines, and data analytics architectures) alongside traditional cloud-native engineering. * Act as the primary technical escalation point for complex infrastructure, application modernization, and data engineering challenges. Anticipates and mitigates technical risks before they impact the client relationship; transforms high-friction escalation scenarios into long-term strategic wins, strengthening the firm's partnership with the enterprise accounts. * Collaborate with Solution Architects/Pre-Sales to scope complex technical engagements, validate engineering hours, and ensure accurate pre-sales technical feasibility. * Drive the professional development, technical certification pathways (specifically scaling Google Cloud and AI specializations), and performance evaluation metrics for the engineering team. * Identify and advocate for internal automation, reusable infrastructure-as-code (IaC) templates, and process improvements to accelerate technical delivery timelines. * Maintain active Google Cloud certifications, achieving the Google Certified Professional Cloud Architect (PCA) designation within the first 90 days of hire. Preferred AI/ML Architectural Competencies: * Design and architect scalable AI infrastructure on Google Cloud Platform, leveraging /GE Agent Platform/Vertex AI and GKE for high-availability model serving. * Demonstrate deep expertise in Large Language Model (LLM) fine-tuning, prompt engineering, and deployment strategies tailored for enterprise-grade generative AI applications. * Architect robust data engineering pipelines for AI, incorporating BigQuery, Dataflow, and Pub/Sub to manage complex data orchestration and feature engineering. * Implement MLOps best practices, including automated model CI/CD, monitoring, and governance frameworks using Vertex AI Pipelines and specialized GCP tooling. ## Related Videos - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Implementing Feature Environments with AWS and Terraform](https://www.wearedevelopers.com/videos/531-implementing-feature-environments-with-aws-and-terraform) - [Making Data Warehouses fast. 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