Senior Cloud Security Automation & AI Engineer- Remote (Anywhere in the U.S.)

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

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)

Travel Requirements

Up to 20% travel

Physical Requirements

Sedentary work

Substantial movement of the wrists, hands, and/or fingers for a minimum of 8 hours a day

Required to have close visual acuity to view computer terminal and/or extensive reading for a minimum of 8 hours a day