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About This Role
At Fortinet, we are at the forefront of securing the evolving Artificial Intelligence (AI) landscape with our FortiAIGate AI security platform.
We are seeking a passionate and experienced AI Sales Specialist to support the go-to-market (GTM) strategy and execution for our rapidly growing AI security solutions. This critical quota carrying sales role will be instrumental in accelerating the adoption of our solutions that help customers secure the creation and usage of AI applications, including homegrown AI and popular agentic AI tools.
Your Responsibilities
- Partner with regional sales leaders to shape and execute the GTM strategy for AI Security solutions across your region, focusing on both expanding existing Fortinet customers and driving greenfield acquisition.
- Collaborate closely with Fortinet account managers and partners to build pipeline, and to identify, qualify, and close AI Security business.
- Lead customer-facing activities, including executive presentations, discovery workshops, and live product demonstrations.
- Design and deliver regional training programs for Fortinet account managers, educating them on new product features, capabilities, competitive differentiators, use cases and business outcomes, to ensure they can confidently pitch and represent Fortinet’s AI security strategy.
- Consult with enterprise clients to evaluate their current AI landscape, identify security challenges, and architect effective solutions. Act as a trusted thought leader on AI governance and controls, runtime protection, guardrails, automated red-teaming, and shadow AI.
- Personally engage in strategic account selling, business development, and ongoing customer success, with a focus on business outcomes and ROI
- Engage deeply with Fortinet’s channel and reseller partners to equip them with the knowledge and resources needed to successfully close AI Security deals, ensuring they are positioned and motivated to lead with Fortinet for AI security.
- Cultivate strong, trusted relationships with key customer stakeholders, including C-level executives, security leaders, and AI decision-makers.
- Partner closely with sales, marketing, and product management to ensure alignment and effective GTM execution and AI Security messaging.
- Serve as an AI evangelist to educate customers on agentic AI best practices, secure AI deployment, and position Fortinet as the definitive leader in AI security projects.
QualificationsYour Experience* Bachelors degree in engineering, science, computer science or related technical field of study prefered
- 7+ years of quota-carrying technical sales experience, including at least 3 years specializing in AI and related technologies.
- Proven track recorder of consistently meeting or exceeding quota and successfully closing multi-stakeholder, 6- and 7-figure ACV deals in complex enterprise accounts
- Demonstrated experience engaging with C-level executives, including CIOs, CISOs, CTOs, VPs of Engineering, and Heads of AI.
- Understanding of core AI technologies, including machine learning, deep learning, and generative AI.
- Experience working within regulated or data-sensitive industries (such as financial services, healthcare, and semiconductors) is preferred.
- Familiarity with popular agentic AI tools, AI infrastructure, and their associated security implications is preferred.
- Exemplary written, verbal, and presentation communication skills.
- Strong understanding of cloud computing platforms (e.g., AWS, Azure, GCP)
- Ability to travel up to 50% to develop customer relationships and advance opportunities
Fortinet offers employees a variety of benefits, including medical, dental, vision, life and disability insurance, 401(k), 11 paid holidays, vacation time, and sick time as well as a comprehensive leave program.
Wage ranges are based on various factors including the labor market, job type, and job level. On target earnings for this position is expected to be $220,000 - $260,000 per year. Exact salary offers will be determined by factors such as the candidate's subject knowledge, skill level, qualifications, experience, and geographic location.
All roles are eligible to participate in the Fortinet equity program, and this position is also eligible for commissions based on the terms of the Sales Compensation Plan
Salary Context
This $220K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fortinet, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($240K) sits 13% above the category median. Disclosed range: $220K to $260K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Fortinet AI Hiring
Fortinet has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Miami, FL, New York, NY, US, Sunnyvale, CA, US. Compensation range: $260K - $260K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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