Gruve
Website:
gruve.ai
Job details:
About Gruve
Gruve is an innovative software services startup dedicated to transforming enterprises to AI powerhouses. We specialize in cybersecurity, customer experience, cloud infrastructure, and advanced technologies such as Large Language Models (LLMs). Our mission is to assist our customers in their business strategies utilizing their data to make more intelligent decisions. As a well-funded early-stage startup, Gruve offers a dynamic environment with strong customer and partner networks.
Position Summary
We are seeking a highly experienced Senior AI Cloud & Security (Multi-Cloud) to serve as the Architectural Deployment Lead for our high-stakes AI Professional Services delivery. The role involves leading the end-to-end deployment strategy for complex AI stacks across Multi-Cloud (AWS, GCP), Hybrid, and strictly On-Prem environments. You will act as the Security & Compliance Authority, enforcing hardening standards, Zero-Trust network access, and global compliance frameworks (GDPR, HIPAA, SOC2) for our AI deliveries. As a technical leader, you will bridge the gap between AI development and customer environment integration, overseeing critical implementations such as AI SOC, OpenShift AI, and Cybersecurity AI Agents.
Key Responsibilities
- Architectural Leadership: Lead the deployment strategy and execution for Enterprise AI Infrastructure, including AI SOC, OpenShift AI, and AI-based Cybersecurity Log Optimization services.
- Security & Compliance: Define and enforce security hardening standards and Zero-Trust architectures for all AI deliveries, ensuring full compliance with GDPR, HIPAA, and SOC2 frameworks.
- Process Automation: Develop standardized deployment blueprints and "Infrastructure as Code" (IaC) templates to ensure repeatable, error-free customer rollouts and minimize manual intervention.
- Complex Implementation: Manage complex tenant isolation, Software Defined Networking (SDN), and storage integration for Managed AI Service Providers (MSSPs) and Model Context Protocol (MCP) servers.
- Production Support: Act as the highest technical point of contact for troubleshooting critical deployment failures, performance bottlenecks, and network connectivity issues in production environments.
- Mentorship: Mentor junior deployment engineers and provide technical guidance to ensure best practices in AI stack deployment and security.
- Service Enablement: Drive the implementation of specialized services including Cybersecurity AI Agents and MCP Security Implementation.
- Manage and optimize GPU resource allocation within OpenShift/Kubernetes (e.g., NVIDIA GPU Operator, MIG - Multi-Instance GPU) to ensure high utilization and cost-efficiency for large-scale inference workloads.
- Architect 'Sovereign Cloud' patterns for strictly regulated regions, ensuring AI model training data and inference logs never cross geopolitical or organizational boundaries.
- Implement cloud cost-governance guardrails specifically for AI services (e.g., managing high-cost GPU instances, Bedrock/Azure OpenAI token usage monitoring) to prevent 'bill shock' during scaling.
- Establish CI/CD/CD (Continuous Deployment & Continuous Diligence) pipelines for Cybersecurity AI Agents, ensuring model updates don't break security hardening or network routing.
Basic Qualifications
- Bachelor’s or master’s degree in computer science, Engineering, or a related field
- 8 - 12 years of experience in Cloud Architecture, DevOps, or Network Security, with a focus on large-scale infrastructure deployment.
- Multi-Cloud & On-Prem Expertise: Deep hands-on experience deploying complex stacks across AWS, Azure, GCP, and strictly air-gapped On-Prem environments.
- Container Orchestration: Expert-level knowledge of Red Hat OpenShift and Kubernetes, specifically for AI workloads and complex storage/networking integrations.
- Proven experience with Zero-Trust Network Access (ZTNA), security hardening, and compliance implementation (SOC2, GDPR).
- Automation Skills: Advanced proficiency in "Infrastructure as Code" (IaC) using Terraform, Ansible, or similar tools to automate deployments.
- Networking Knowledge: Strong understanding of SDN, complex routing, tenant isolation, and secure network architecture for MSSPs.
- Problem Solving: Exceptional ability to troubleshoot high-stakes production issues and performance bottlenecks in AI infrastructure.
- Hands-on experience with Service Mesh (Istio/Linkerd) for mTLS-based communication between AI microservices and vector databases.
- Experience deploying and securing vector databases (e.g., Pinecone, Milvus, Weaviate) in high availability, clustered configurations.
- Proficiency in setting up observability stacks (Prometheus, Grafana, OpenTelemetry) to monitor not just system health, but also model latency and inference drift.
- Familiarity with securing MCP servers against prompt injection and unauthorized tool execution at the infrastructure level.
- Compliance Experience: Direct experience auditing or implementing HIPAA/SOC2 controls in a technical environment.
- Certifications: AWS/GCP Solution Architect, HashiCorp Certified: Terraform Associate, AWS/GCP Certified DevOps Engineer.
Preferred Qualifications
- Certifications: Certified Kubernetes Administrator (CKA), AWS Certified AI Practitioner.
- AI Infrastructure: Experience with AI SOC rollouts, Model Context Protocol (MCP) servers, or AI-based security agents.
Why Gruve
At Gruve, we foster a culture of innovation, collaboration, and continuous learning. We are committed to building a diverse and inclusive workplace where everyone can thrive and contribute their best work. If you’re passionate about technology and eager to make an impact, we’d love to hear from you.
Gruve is an equal opportunity employer. We welcome applicants from all backgrounds and thank all who apply; however, only those selected for an interview will be contacted.
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