Position Summary:

We are seeking a highly experienced Senior Security Consultant (Cloud Deployment Engineer) 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.