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About This Role
AI Solutions Engineer
Location: Remote
Compensation: $67,000 \- $94,000
Employment Type: Full\-Time
Travel Requirement: Regular travel between 15\-25%
Clearance: Must be eligible for a U.S. Secret Clearance
What you can expect as the AI Solutions Engineer at Fortress...
*The AI Solutions Engineer is the rapid prototyper at the center of Fortress's forward deployed engineering capability. This role turns urgent customer and internal problems into working solutions fast, using modern AI\-assisted development platforms (such as Lovable, Claude, Retool, and similar tools) to build functional prototypes, automations, and internal tooling in days rather than months. This role works closely with Solutions Engineering, Product, and senior forward deployed engineers. The AI Solutions Engineer validates ideas quickly, demonstrates value to customers and stakeholders, and hands off proven concepts to Product and Engineering with clear requirements so they can be scaled into supported capabilities. This role is designed as a growth path: high performers will progressively develop the data engineering, cybersecurity, and customer\-facing skills to move toward the Forward Deployed Engineer track. This is an ideal opportunity for a hands\-on builder who moves fast, learns tools quickly, and cares more about shipping working solutions than about any particular technology stack.*
Responsibilities Include
- Rapidly build working prototypes of AI\-enabled tools, workflows, and applications using platforms such as Lovable, Claude, Retool, and similar AI\-assisted development tools.
- Turn loosely defined problem statements from customers, Solutions Engineering, and internal teams into functional demos and proofs of concept.
- Build and iterate on internal tooling, dashboards, and automations that reduce manual effort and improve time\-to\-resolution.
- Connect prototypes to real data through APIs, integrations, and data sources (e.g., Supabase or similar backends).
- Demo prototypes to internal stakeholders and, over time, to customers; incorporate feedback in fast iteration cycles.
- Document what was built and hand off validated concepts to Product Management and Engineering with clear requirements so they can be scaled into supported capabilities.
- Follow responsible development practices: version control, access hygiene, and appropriate use of AI tools within company standards.
- Pair with senior forward deployed engineers, progressively taking on more technical depth and customer\-facing responsibility.
- Other duties as assigned.
Minimum Qualifications
- Demonstrated ability to build working software or applications using AI\-assisted and low\-code development platforms (e.g., Lovable, Claude, Retool, n8n, or similar).
- Comfort working with APIs, data sources, and integrations to make prototypes function with real data.
- A builder's mindset: speed, curiosity, pragmatism, and the judgment to know when a prototype is good enough to prove the point.
- Ability to take a vague problem statement and turn it into something demonstrable without detailed specifications.
- Clear written and verbal communication, including demoing work to technical and non\-technical audiences.
- Willingness to learn Fortress platform capabilities, customer workflows, and cybersecurity domain concepts.
- Must be eligible for a U.S. security clearance; successful completion of a background check is required.
- 2\+ years’ experience in a related field.
Preferred Skills
- Exposure to scripting or light coding (Python, JavaScript, or SQL) beyond low\-code platforms.
- Experience with Supabase, Postgres, or similar backends, and with authentication concepts (e.g., SSO).
- Familiarity with LLM concepts such as prompt engineering, RAG, or agent workflows.
- Experience in SaaS, cybersecurity, supply chain, risk management, or enterprise software.
- A portfolio of things you have built \- side projects, internal tools, or shipped prototypes.
- Interest in growing into a customer\-embedded Forward Deployed Engineer role.
Education
- Bachelor's degree in computer science, engineering, cybersecurity, or a related field required.
Employee Benefits
- Remote and Hybrid working environment
- Competitive pay structure
- Medical, dental, vision plans with employees covered up to 90% with highly progressive options for dependents and families
- Company paid life, short\- and long\-term disability insurance
- Employee Assistance Program
- 401(k) match
- Flexible Paid Time Off
- Parental Leave
Employment Perks
- We provide each employee with professional growth opportunities through succession planning, up\-skilling, and certifications
- Tuition and certification reimbursement
- Employee Referral Programs
- Company Sponsored Events
Fortress is proud to be an Equal Opportunity Employer. All employees and applicants will receive consideration for employment without regard to age, color, disability, gender, national origin, race, religion, sexual orientation, gender identity, protected veteran status, or any other classification protected by federal, state, or local law. Fortress Information Security takes part in the E\-Verify process for all new hires.
For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will have to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case\-by\-case basis.
Salary Context
This $67K-$94K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Fortress Information 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($80K) sits 63% below the category median. Disclosed range: $67K to $94K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Fortress Information Security AI Hiring
Fortress Information Security has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $94K - $94K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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