Interested in this AI/ML Engineer role at Aptasentry?
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About This Role
About AptaSentry
AptaSentry is an AI\-powered cybersecurity platform helping organizations identify, test, and reduce risks in their AI systems, applications, and workflows. As companies adopt tools like ChatGPT, Gemini, Claude, copilots, and custom AI models, they face new risks around sensitive data exposure, prompt injection, model misuse, compliance gaps, and unsafe AI behavior.
AptaSentry helps businesses safely deploy AI by providing AI security testing, red\-teaming, risk monitoring, and governance support.
Role Overview
We are looking for a motivated Cybersecurity Product Sales Representative to help drive customer growth for AptaSentry. This person will identify prospects, run outreach, qualify leads, explain the product clearly, schedule demos, and help close deals with companies adopting AI and cybersecurity solutions.
This role is ideal for someone who understands B2B software sales, cybersecurity, AI, or SaaS, and can communicate technical value in a simple, business\-focused way.
Responsibilities
- Identify and reach out to potential customers, including CISOs, CTOs, security leaders, compliance teams, AI governance teams, and enterprise technology leaders.
- Build and manage a pipeline of prospects through email, LinkedIn, calls, referrals, and industry research.
- Explain AptaSentry’s value proposition around AI red\-teaming, model testing, data protection, and AI security risk reduction.
- Schedule and support product demos with qualified prospects.
- Understand customer pain points related to AI adoption, cybersecurity, privacy, compliance, and enterprise risk.
- Work with the product and leadership team to improve sales messaging, customer targeting, and go\-to\-market strategy.
- Track outreach activity, leads, meetings, and deal progress in a CRM or shared tracking system.
- Help create sales materials, pitch decks, follow\-up emails, and proposal language.
- Provide feedback from customers to improve product positioning and features.
Ideal Candidate
- Experience in B2B sales, SaaS sales, cybersecurity sales, AI product sales, or enterprise software sales.
- Comfortable reaching out to new prospects and starting conversations.
- Strong communication skills and ability to explain technical products in clear business language.
- Basic understanding of cybersecurity, AI, data privacy, or enterprise software.
- Self\-motivated, organized, and able to work independently.
- Comfortable with LinkedIn outreach, cold email, discovery calls, and demo scheduling.
- Able to learn quickly and adapt messaging based on customer feedback.
Preferred Qualifications
- 1–3\+ years of sales experience in SaaS, cybersecurity, cloud, AI, or enterprise software.
- Existing network with security leaders, technology executives, or enterprise buyers.
- Familiarity with concepts like AI governance, prompt injection, red\-teaming, SOC 2, ISO 27001, data leakage, model risk, or compliance.
- Experience using CRM tools such as HubSpot, Salesforce, Apollo, ZoomInfo, or LinkedIn Sales Navigator.
- Experience selling to mid\-market or enterprise customers.
Compensation
Compensation will depend on experience and role structure.
Possible structures include:
- Base \+ Commission\-Based Option: Higher commission percentage for contractors or part\-time sales partners
- Equity/Bonus Potential: Available for strong early contributors, depending on fit and performance
Why Join AptaSentry?
- Work on one of the fastest\-growing areas in cybersecurity: AI security.
- Help companies safely adopt AI while reducing real business risk.
- Join an early\-stage team where your work directly impacts growth.
- Opportunity to shape sales strategy, messaging, and customer development from the ground up.
- High upside for someone who can build pipeline and close early customers.
How to Apply
Please send your resume and a short note explaining your sales experience and interest in AI cybersecurity to: [email protected]
Pay: $50,000\.00 \- $80,000\.00 per year
Benefits:
- Flexible schedule
- Professional development assistance
Work Location: Remote
Salary Context
This $50K-$80K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Aptasentry, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($65K) sits 64% below the category median. Disclosed range: $50K to $80K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Aptasentry AI Hiring
Aptasentry has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $80K - $80K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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