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About This Role
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.
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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
We use Greenhouse Software as our applicant tracking system and Zoom Scheduler for HR screen request scheduling. At times, your email may block our communication with you. Please be sure to check your SPAM folder so that you don't miss updates on your application.
Why GuidePoint?
GuidePoint Security is a rapidly growing, profitable, privately-held value added reseller that focuses exclusively on Information Security. Since its inception in 2011, GuidePoint has grown to over 1,300 employees, established strategic partnerships with leading security vendors, and serves as a trusted advisor to more than 6,200 customers.
Firmly-defined core values drive all aspects of the business, which have been paramount to the company's success and establishment of an enjoyable workplace atmosphere. At GuidePoint, your colleagues are knowledgeable, skilled, and experienced and will seek to collaborate and provide mentorship and guidance at every opportunity.
This is a unique and rare opportunity to grow your career along with one of the fastest growing companies in the nation.
Some added perks….
- Remote workforce primarily (U.S. based only, some travel may be required for certain positions, working on-site may be required for Federal positions)
- Group Medical Insurance options: Zero Deductible PPO Plan (GuidePoint pays 90% of the premium for employees and 70% for family plans (spouse/children/family) or High Deductible Health Plan with HSA (GuidePoint pays 100% of the employees premiums and 75% for family plans (spouse/children/family). If you choose the High Deductible / HSA plan, GPS will contribute in 4 equal quarterly installments: ($850 per EE annually / $1750 per family annually (includes spouse/children/family options)
- Group Dental Insurance: GuidePoint pays 100% of the premium for employees and 75% of family plans
- 12 corporate holidays and a Flexible Time Off (FTO) program
- Healthy mobile phone and home internet allowance
- Eligibility for retirement plan after 2 months at open enrollment
- Pet Benefit Option
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At GuidePoint Security, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
GuidePoint Security AI Hiring
GuidePoint Security has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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