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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
The GuidePoint Security North Central region is seeking a skilled AI Security Engineer to join our growing AI Security Services team within the Automation and AI Practice. You will assist customers in the design, implementation, security, and operational management of generative AI security solutions — including AI governance assessments, LLM and agent security testing, shadow AI discovery, agentic workflow development, and architecture reviews.
About the North Central AI Security Practice
You will work closely with peers across multiple domains including AppSec, Cloud Security, and Identity and Access Management to deliver holistic and secure solutions adhering to industry best practices. This role offers the opportunity to contribute directly to the continued growth of our AI security practice and its expanding service portfolio.
Roles and Responsibilities:
- AI Security Architecture & Assessment: Conduct secure configuration reviews and security assessments of enterprise AI platforms (e.g., Anthropic Claude Enterprise, OpenAI ChatGPT Enterprise, Microsoft Copilot) against established control domains — including identity and provisioning, network and access enforcement, data protection and retention, and audit and compliance — identifying vulnerabilities, attack surfaces, and gaps against industry frameworks (e.g., OWASP LLM Top 10, MITRE ATLAS)
- Threat Modeling for AI Systems: Lead threat modeling exercises specific to AI workloads, covering prompt injection, model inversion, data poisoning, supply chain risks, excessive agency, privilege escalation through tool chaining, and unauthorized cross-application data movement across SaaS, self-hosted, and local AI deployments
- AI Coding Tool & Development Environment Security: Assess AI coding tools and development environments — including Claude Code, OpenAI Codex, Open Code, Cursor, and MCP servers — for sandbox isolation, plugin allowlisting, secrets access, network egress controls, and CI/CD pipeline security
- Secure AI Integration Guidance: Advise client teams on securely integrating SaaS AI services and APIs (e.g., OpenAI, Azure OpenAI, AWS Bedrock) into enterprise applications, including safe handling of credentials, outputs, and user data
- Data Security & Privacy Controls: Evaluate and recommend controls for data ingestion pipelines, RAG architectures, and vector databases to prevent unauthorized data exposure, leakage through model outputs, or non-compliant data processing — including zero data retention enforcement, sensitivity labeling, data classification, and encryption key management
- Shadow AI Discovery: Conduct shadow AI discovery engagements to inventory unsanctioned AI tool usage and assess associated data exposure risks across client environments
- Security Controls & Guardrails Evaluation: Evaluate security controls and guardrails across AI platforms, including identity and access management, DLP integration, conditional access policies, SIEM and logging configurations, human-in-the-loop mechanisms, and AI-specific runtime protections
- Agentic Workflow Development: Design, build, and deploy AI agent workflows including use-case design, agent architecture, and integration with security tooling and automation platforms
- Security Architecture Reviews: Perform security architecture reviews for organizations deploying AI agents that connect to tools, data sources, or other agents via MCP or agentic frameworks, delivering reference architectures and recommendations
- Security Documentation & Deliverables: Develop and deliver engagement artifacts including secure configuration review reports, prioritized findings registers, AI security control matrices, reference architectures, risk assessments, control frameworks, and secure enablement roadmaps
- Strategic AI Security Roadmap: Contribute to the development of long-term AI security strategies for clients, including prioritized remediation roadmaps, capability maturity assessments, and investment recommendations
- Collaboration & Stakeholder Engagement: Serve as a trusted security advisor bridging business stakeholders, AI/ML engineers, IT operations, and information security teams — conducting stakeholder interviews and facilitating knowledge transfer sessions covering AI platform administration, troubleshooting, and operational runbooks
- AI Threat Landscape Monitoring: Continuously track emerging AI security research, adversarial techniques, regulatory developments, and vendor security advisories (e.g., Anthropic, OpenAI, Microsoft feature releases) to keep client guidance relevant and proactive
Required Experience and Education:
- 3+ years of experience in security engineering with a significant focus on cloud security and/or application security
- Hands-on experience implementing, managing, securing, and supporting agentic AI solutions within an enterprise context, including enterprise AI platforms such as Anthropic Claude (Enterprise, Desktop, Cowork, Code), OpenAI (ChatGPT Enterprise, Codex, API), or Microsoft Copilot
- Familiarity with major cloud service provider AI-focused services such as AWS Bedrock, AWS SageMaker, Azure AI Foundry, or Google Vertex
- Solid understanding of generative AI concepts, Large Language Models (LLMs), context engineering, agentic tool usage, and foundational AI/ML principles
- Operational experience in the usage of agentic coding assistants such as Claude Code, Open Code, Cursor, or Codex
- Understanding of AI-specific security challenges including prompt injection, data poisoning, supply chain security, model extraction attacks, excessive agency, and privilege escalation through tool chaining
- Ability to produce professional client-facing deliverables including assessment reports, findings registers, control matrices, and reference architecture documentation
- Strong written and verbal communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences
- Embraces emerging technologies, including AI tools, to work smarter, solve problems, and drive better business outcomes
Preferred Experience and Education
- Certifications such as AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer, Azure AI Engineer Associate, or Claude Certified Architect
- Understanding or experience with model fine-tuning techniques
- Experience with policy as code languages like Cedar or Rego and Infrastructure as Code (IaC) tools like AWS CloudFormation, Terraform, OpenTofu, or equivalent technologies
- Experience designing and implementing agentic AI architectures that balance security and autonomy
- Familiarity with MCP client/server architecture vs. agentic skills and the associated security risks of each
- Experience with AI security tooling categories such as AI Security Posture Management (AISPM), AI runtime protection, or AI control plane platforms
- Prior experience in a consulting, professional services, or managed services delivery model with direct client engagement
Travel Requirements:
- Up to 10% 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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 $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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