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About This Role
Who is Trace3?
Trace3 is a leading Transformative IT Authority, providing unique technology solutions and consulting services to our clients. Equipped with elite engineering and dynamic innovation, we empower IT executives and their organizations to achieve competitive advantage through a process of Integrate, Automate, Innovate.
Our culture at Trace3 embodies the spirit of a startup with the advantage of a scalable business. Employees can grow their career and have fun while doing it!
Trace3 is headquartered in Irvine, California. We employ more than 1,200 people all over the United States. Our major field office locations include Denver, Indianapolis, Grand Rapids, Lexington, Los Angeles, Louisville, Texas, San Francisco.
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Street-Smart - Thriving in Dynamic Times
We are flexible and resilient in a fast-changing environment. We continuously innovate and drive constructive change while keeping a focus on the "big picture." We exercise sound business judgment in making high-quality decisions in a timely and cost-effective manner. We are highly creative and can dig deep within ourselves to find positive solutions to different problems.
Juice - The "Stuff" it takes to be a Needle Mover
We get things done and drive results. We lead without a title, empowering others through a can-do attitude. We look forward to the goal, mentally mapping out every checkpoint on the pathway to success, and visualizing what the final destination looks and feels like.
Teamwork - Humble, Hungry and Smart
We are humble individuals who understand how our job impacts the company's mission. We treat others with respect, admit mistakes, give credit where it's due and demonstrate transparency. We "bring the weather" by exhibiting positive leadership and solution-focused thinking. We hug people in their trials, struggles, and failures – not just their success. We appreciate the individuality of the people around us.
JOB SUMMARY:
The Principal Consultant of AI Security will be responsible for providing architect-level thought leadership and expertise in securing enterprise AI programs, including AI models, applications, agents, data, and the pipelines and platforms they run on. Under the direction of AI Security leadership, the Principal Consultant will lead Trace3's most complex client engagements in this domain, serve as a senior technical authority for AI security architecture across the Security Solutions practices, and turn that work into the reference architectures, offerings, and enablement that allow Trace3 associates, partners, and regional teams to help clients adopt and scale AI in a secure, resilient, and trustworthy manner.
SUMMARY OF ESSENTIAL JOB FUNCTIONS:
- Lead client engagements as the Subject Matter Expert (SME) across the full range of AI security services, including assessment, strategy, architecture, and design engagements spanning AI models, applications, agents, data, and the pipelines and platforms they run on, from discovery through executive readout.
- Engage with clients to assess needs and provide strategic, tactical, and operational recommendations that translate AI risk into business terms for CISO-, CIO-, and board-level audiences, and that position security as the enabler of the client's AI program rather than a constraint on it.
- Own and evolve the practice's AI security offerings, and author the reference architectures, standards, and design patterns that allow the Security Solutions practices to deliver one coherent AI security answer to a client rather than several overlapping ones.
- Collaborate with a multidisciplinary team across Security Solutions (Governance, Risk & Compliance; Identity; Cloud and Application Security; Data Security; Security Operations; Cyber Resiliency), Data & Analytics, Digital Consulting, Innovation, and Service Delivery to scope, design, and deliver programs, solutions, and services engagements that enable secure and responsible AI transformation.
- Develop and maintain Trace3's point of view on emerging AI security problems, including trustworthy agents, agent-to-agent trust and delegation, non-human identity, and AI resilience, and contribute that point of view to Trace3's solution plays and go-to-market initiatives.
- Develop and deliver enablement for regional solutions architects and sellers, including discovery and qualification guidance for AI security opportunities and delivery playbooks for standardized assessment work, such that AI security can be recognized, opened, and sold by the field without the Principal in every conversation.
- Foster and contribute to partnerships with AI security and platform vendors, hyperscalers, delivery partners, industry groups, and research institutions, influencing roadmaps, shaping joint offerings, and ensuring client and organizational needs are consistently exceeded.
- Advocate on behalf of Trace3 on the importance of AI security through speaking engagements, publications, client and partner briefings, and participation at industry events and forums.
- Provide subject matter expertise with respect to AI security, including staying up to date on the latest threats, techniques, technologies, regulatory requirements, industry standards, frameworks, and best practices, and with the technologies that operationalize AI security controls.
- Mentor consultants, solutions architects, and interns within the practice and across regions, transferring judgment as well as knowledge, and contributing to Trace3's leadership and talent development standards.
REQUIRED SKILLS AND EXPERIENCE:
- Bachelor's degree in computer science, Cybersecurity or equivalent information security, engineering, data science, or like discipline from an accredited college or university, or measurable knowledge / experience from proven industry, military, defense, or government operations.
- Experience in a customer-facing technology consulting, solutions architecture, or professional services role.
- 10+ years' experience developing, implementing, and managing Cybersecurity strategies, programs, architectures, technologies, and associated controls.
- 3+ years' experience securing AI/ML systems in production, including at least two of: model and AI supply chain security, LLM and AI application security, agentic AI or autonomous system security, AI data and pipeline security, or AI red teaming and adversarial testing.
- Demonstrated ability to design and defend AI security architectures for enterprise environments, including controls for AI agents, non-human identities, delegated access, tool invocation, and constrained action across systems.
- Subject matter expertise of AI security and Trustworthy AI Principles, Frameworks and Standards including, but not limited to NIST AI RMF, ISO 42001:2023, ISO 5338:2024, OWASP Top 10 for LLM Applications and Agentic Applications, MITRE ATLAS, and CSA AI controls.
- Subject matter expertise of Cybersecurity Principles, Frameworks and Standards including NIST CSF, CSA CCM, MITRE ATT&CK, and ISO 27001.
- Working knowledge of AI governance and regulatory requirements (including the EU AI Act and emerging U.S. state and sector requirements) sufficient to partner effectively with Governance, Risk & Compliance on AI risk programs.
- Working knowledge and hands-on experience with at least one (preferably multiple) major cloud service providers (AWS, GCP, Azure), their AI platforms and services, and their native AI security capabilities.
- Knowledge and hands-on experience with technologies that operationalize AI security, including but not limited to AI security posture management, LLM gateways and guardrails, AI runtime protection, agent identity and authorization, model registries and AI supply chain controls, and AI-aware data security.
- Ability to collaborate cross-functionally with software engineers, data scientists, platform teams, and security personnel to assess security risks and requirements throughout the AI development and deployment lifecycle.
- Certifications including CISSP, CCSP, AIGP, AAISM, or cloud AI/security certifications from at least one major CSP are highly preferred.
- Prior experience packaging offerings, authoring reference architectures, or running technical enablement or certification programs is highly preferred.
- Public technical voice, including talks, published research, or open-source contributions in AI security, is highly preferred.
- Ability to conduct training workshops, assessments, and executive briefings and to develop clear, concise reporting and recommendations.
- Knowledge and experience creating and maintaining technical documentation, presentations, plans, roadmaps, etc.
- Strong executive communication skills, including the ability to make complex AI security problems understandable to executives and to defend a recommendation under challenge.
- Strong interpersonal and communication skills are required.
- Strong customer presentation skills are required.
- Motivated self-starter who loves to solve challenging problems and feels comfortable working directly with customers.
- Highly organized, detail-oriented, excellent time management skills and able to effectively prioritize tasks in a fast-paced, high-volume, and evolving work environment.
- Comfortable managing multiple and changing priorities, and meeting deadlines in an entrepreneurial environment.
- Ability to travel when needed (approximately 25–35%); holds a valid driver's license.
The Perks
- Comprehensive medical, dental and vision plans for you and your dependents
- 401(k) Retirement Plan with Employer Match, 529 College Savings Plan, Health Savings Account, Life Insurance, and Long-Term Disability
- Competitive Compensation
- Training and development programs
- Major offices stocked with snacks and beverages
- Collaborative and cool culture
- Work-life balance and generous paid time off
Our Commitment
At the core of Trace3's DNA is our people. We are a diverse group of talented individuals who understand the importance of teamwork and demonstrating leadership, character, and passion in all that we do.
We're committed to fostering an inclusive workplace where everyone feels respected, valued, and empowered to grow. We recognize that embracing diversity drives innovation, improves outcomes, fosters collaboration, boosts teammate satisfaction, and builds a more inclusive culture.
As an equal opportunity employer, Trace3 bases all employment decisions based on individual qualifications, merit, and business requirements. We do not engage in discrimination on the basis of race, color, religion, sex (including gender identity, sexual orientation, and pregnancy), national origin, age (40 or older), disability, genetic information, or any other characteristic protected by federal, state, or local law.
Any demographic information provided is strictly voluntary, kept confidential in accordance with Equal Employment Opportunity (EEO) regulations, and will not be used in employment decisions, including hiring, promotions, or mentorship programs. We are committed to providing equal employment opportunities for all.
If you require a reasonable accommodation to complete the application process or participate in an interview, please email [email protected].
To all recruitment agencies:* Trace3 does not accept unsolicited agency resumes/CVs. Please do not forward resumes/CVs to our careers email addresses, Trace3 employees or any other company location. Trace3 is not responsible for any fees related to unsolicited resumes/CVs.
Salary Context
This $190K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Trace3, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $190K to $250K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Trace3 AI Hiring
Trace3 has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Dallas, TX, US, Louisville, KY, Irvine, CA. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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