Senior Cloud Security Automation & AI Engineer

GuidePoint Security provides trusted cybersecurity expertise, solutions, and services that help organizations make better decisions and minimize risk. By taking a three-tiered, holistic approach for evaluating security posture and ecosystems, GuidePoint enables some of the nation’s top organizations, such as Fortune 500 companies and U.S. government agencies, to identify threats, optimize resources, and integrate best-fit solutions that mitigate risk.

General Description

We are looking for a skilled Senior Cloud Security Automation & AI Engineer to support the Cloud Security Automation and AI Practice. This role combines hands-on delivery, technical oversight, presales support, and practice development to help organizations adopt and secure AI/ML platforms across multi-cloud environments.

The Senior Cloud Security Automation & AI Engineer will be required to demonstrate strong technical depth across cloud-native AI services, agentic AI design patterns, and AI governance frameworks. This individual will compose and secure agentic AI solutions, implement AI gateways and policy-based controls, and translate complex security and automation requirements into actionable, outcome-driven solutions. They will lead by influence and bring business acumen to drive the adoption of progressive cloud security automation programs aligned with client objectives and practice priorities.

About the Cloud Security Automation and AI Practice

The Cloud Security Automation and AI Practice is responsible for helping organizations securely adopt, deploy, and govern AI/ML workloads and automation pipelines across cloud environments. We deliver advisory, implementation, and managed services that bridge the gap between innovation and security.

Our team of engineers, architects, and consultants focuses on cloud-native AI platforms, security automation frameworks, and enterprise AI governance. We partner with clients, account executives, and technology vendors to deliver solutions that reduce risk while accelerating AI adoption.

• Deliver secure AI/ML platform implementations across AWS, Azure, Google Cloud, and third-party enterprise AI platforms
• Develop reusable automation frameworks, accelerators, and reference architectures for AI security
• Drive thought leadership and practice growth through presales support, content development, and industry engagement

Roles and Responsibilities

Delivery & Technical Execution

• Lead end-to-end delivery of cloud security automation and AI engagements, including scoping, architecture design, implementation, and client handoff
• Design and implement secure agentic AI solutions, including multi-agent orchestration, Model Context Protocol (MCP) integrations, AI gateway architectures, and policy-based access controls using frameworks such as Cedar
• Architect and enforce AI governance policies, including usage policies, data handling controls, model access management, and compliance guardrails for enterprise AI deployments
• Develop and deploy AI-powered security automation solutions (e.g., automated compliance checks, threat detection agents, remediation workflows) for clients
• Produce high-quality deliverables including architecture documents, runbooks, SOPs, and security assessment reports

Technical Oversight & Quality Assurance

• Provide technical oversight and quality assurance across active engagements, ensuring deliverables meet GuidePoint standards and client expectations
• Mentor and guide junior engineers on best practices for cloud security, AI/ML implementation, and secure development
• Conduct architecture reviews, code reviews, and security assessments for AI/ML workloads

Presales & Business Development Support

• Support presales activities by participating in client discovery calls, demos, and technical deep dives
• Contribute to proposals, statements of work (SOWs), and pricing estimates for AI security and automation engagements
• Collaborate with account executives and practice leadership to identify opportunities and shape client solutions

Practice Development & Thought Leadership

• Contribute to practice development by building reusable tools, templates, accelerators, and reference architectures
• Develop thought leadership content such as blog posts, whitepapers, webinars, and conference presentations
• Stay current on emerging AI/ML platforms, cloud security trends, and regulatory developments to inform practice strategy

Required Experience and Education

• A Bachelor’s Degree (BS/BA) + 5-7 years of experience
• Demonstrated experience designing, implementing, or securing AI/ML workloads in cloud environments
• Hands-on proficiency with primary AI/ML platforms: Amazon Bedrock, Amazon Q, and Amazon SageMaker
• Preferred experience with secondary platforms: Azure AI Foundry, Microsoft 365 Copilot, Copilot Studio, Azure Machine Learning
• Experience in a client-facing consulting or professional services role
• Embraces emerging technologies, including AI tools, to work smarter, solve problems, and drive better business outcomes
• Basic Python competency, including the ability to read, write, and troubleshoot code for automation and integration tasks
• Understanding of agentic AI patterns, AI governance frameworks, and secure AI composition (e.g., multi-agent orchestration, tool-use guardrails, prompt injection mitigation)

Technology Proficiency

Primary (AWS)

• Amazon Bedrock
• Amazon Q (Business, Developer)
• Amazon SageMaker

Secondary (Azure)

• Azure AI Foundry
• Microsoft 365 Copilot
• Copilot Studio
• Azure Machine Learning

Additional Platforms

• Gemini for Google Workspace
• Claude for Enterprise (Anthropic)
• ChatGPT Enterprise / OpenAI Platform

AI Security & Governance

AI Gateways (centralized proxy, traffic control, policy enforcement)

Model Context Protocol (MCP)

Cedar (AWS policy language for fine-grained authorization)

AI governance and usage policy frameworks (NIST AI RMF, ISO 42001, OWASP LLM Top 10)

Preferred Experience and Education

• AWS Certified Solutions Architect, AWS Certified Machine Learning, or AWS AI Practitioner certification
• Azure AI Engineer Associate, Azure Solutions Architect Expert, or Microsoft 365 Certified: Administrator Expert
• Google Cloud Professional Machine Learning Engineer or Google Cloud Professional Cloud Architect
• Experience writing statements of work (SOWs), proposals, or scoping documents for professional services engagements
• Background in security frameworks (NIST AI RMF, OWASP LLM Top 10, ISO 42001) as applied to AI/ML workloads
• Public speaking experience, including presentations at conferences, webinars, or industry events
• Prior experience in practice development, service catalog creation, or go-to-market strategy for a consulting or professional services firm
• AWS Certified Developer - Associate
• Experience with AI gateways, Model Context Protocol (MCP), and policy-as-code frameworks (e.g., Cedar, OPA)