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About This Role
Who We Are
Ontic provides software that helps corporate and government security teams identify threats, assess risk, and respond faster to keep people and organizations safe. Its AI\-powered Connected Intelligence Platform unifies security operations and data into a centralized system of record, enabling organizations to conduct risk assessments, protect against workplace violence, and manage threats and incidents more efficiently. Fortune 500 companies and federal agencies rely on Ontic to support security programs such as executive protection, threat intelligence, and corporate investigations. Learn more at ontic.ai or follow us on LinkedIn.
Who You Are
We are seeking an experienced and forward\-thinking AI Security Engineer to lead the development and implementation of security controls, governance frameworks, and risk management practices for Artificial Intelligence (AI) technologies across the organization. This role will be responsible for helping define AI usage policies, assessing risks associated with AI adoption, implementing security guardrails, and ensuring AI systems are deployed responsibly and securely.
The ideal candidate combines expertise in cybersecurity, risk management, and emerging AI/ML platforms with a strong understanding of governance, compliance, and secure development practices.
Responsibilities
- Develop, maintain, and enforce policies and procedures governing the use of AI technologies
- Partner with Legal, Privacy, and Technology teams to establish responsible AI governance frameworks
- Monitor evolving regulatory requirements and industry standards related to AI security, privacy, and governance
- Conduct security and risk assessments of AI platforms, models, and applications
- Review proposed AI use cases and provide security recommendations prior to deployment
- Develop risk mitigation strategies and compensating controls for identified AI\-related threats
- Design and implement technical guardrails for AI platforms and applications
- Develop secure integration patterns for AI services and APIs
- Evaluate and recommend AI security tools and technologies
- Collaborate with engineering teams to integrate security controls into AI\-enabled applications
- Develop monitoring and detection capabilities for AI\-related security events
- Provide guidance and training to employees on secure and responsible AI usage
Qualifications
- BA/BS or higher in Cybersecurity, Computer Science, Information Technology, Management Information Systems, or a related field
- 5\+ years experience in information security\-related roles
- Knowledge of AI technologies, Large Language Models (LLMs), generative AI platforms and AI development tools
- Direct, hands\-on experience with AI platforms such as OpenAI (ChatGPT, Codex), Anthropic (Claude Chat, Code, Cowork), Cursor, Google Gemini and AWS Bedrock
- Experience with cloud security or application security
- Deep understanding of the NIST AI Risk Management Framework, ISO42001 OWASP Top 10 for LLM Applications and MITRE ATLAS
- Professional certifications such as CISSP, CCSP, CISM, ISO42001 or other AI security certification
- Excellent written and verbal communication skills
- Extremely organized and able to oversee multiple projects simultaneously
- Ability to travel to Austin TX occasionally and to India 2 times a year
Ontic Benefits \& Perks
Competitive Salary
Medical, Vision \& Dental Benefits
401k
Stock Options
HSA Contribution
Learning Stipend
Flexible PTO Policy
Quarterly company ME (mental escape) days
Generous Parental Leave policy
Home Office Stipend
Mobile Phone Reimbursement
Home Internet Reimbursement for Remote Employees
Anniversary \& Milestone Celebrations
Ontic is an equal\-opportunity employer. We are committed to a work environment that celebrates diversity. We do not discriminate against any individual based on race, color, sex, national origin, age, religion, marital status, sexual orientation, gender identity, gender expression, military or veteran status, disability, or any factors protected by applicable law.
All Ontic employees are expected to understand and adhere to all Ontic Security and Privacy related policies in order to protect Ontic data and our clients data.
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At Ontic we are dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
Ontic prioritizes the full inclusion of qualified individuals, providing necessary accommodations for those with disabilities to perform essential job functions. If you need assistance during the application or interview process or job tasks, please contact us at [email protected] or call (512\) 572\-7400
Compensation Range: $140K \- $160K
Salary Context
This $140K-$160K range is below the median 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 Ontic, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 16% below the category median. Disclosed range: $140K to $160K.
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.
Ontic AI Hiring
Ontic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $160K - $160K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national 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 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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