Interested in this AI/ML Engineer role at Optiv?
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*This position will be fully remote and can be hired anywhere in the continental U.S.*
The Principal Advisor, Center of Excellence plays a critical role in serving as a trusted partner to Optiv’s clients. By combining advanced business and security practitioner knowledge, the Principal Advisor designs security solutions using some of the most advanced security services and technologies to achieve highly defensible and scalable security programs to align with the clients’ security initiatives wherever AI is involved. The Principal Advisor has extensive real\-world knowledge in AI and can design pragmatic security solutions tailored to each client’s unique environment and provide our clients and sellers with consistent security expertise on all sales opportunities. In partnership with domain specialists and experts, the Principal Advisor will drive thought leadership and inspired cyber security solutions powered by our ecosystem of people, products, and partners in all areas of AI.
How you'll make an impact
- Drive pipeline generation of services and technologies business to meet or exceed quarterly and annual quota objectives in partnership with the account and domain teams in all areas relating to AI opportunity
- Follows the Optiv Standardize Sales Operating Processes (SOPs) to achieve consistent success
- Maintain expert level knowledge of the client’s security environment, business operations, security needs, and risk appetite
- Identify AI related security concerns and/or potential application of AI security and toolsets and determine how they correlate into Optiv’s strategic solutions across the assigned domain and holistic cyber security programs
- Identify cross\-sell and upsell opportunities across clients and Optiv's partner relationships
- Qualify, lead, and partner with internal colleagues to determine scope, proposal management, and follow through to closure
- Participate in sales opportunities across Optiv's entire portfolio as it relates to AI product, services, and/or advisement opportunity
- Clearly articulate how the necessary elements of the Optiv AI technology and services portfolio meet the specific needs of the client stakeholders at an executive level
- Stay abreast of industry trends, news, and maintain a broad understanding of the AI landscape to facilitate thought leadership, internal and external support, analysis, and guidance to clients and internal Optiv groups
- Collaborate with service delivery to ensure the team has necessary supporting domain specialty materials that presents a consistent and comprehensive approach to AI
- Effectively work with multiple client personas across the executive branch (CxO), as well as other relevant personas to develop AI product and services, advisement, and AI security strategy and define roadmaps to execute on AI strategy\-aligned business goals, budgetary spend, and metrics based on return of investment
- Maintain advisory relationships with key stakeholders at clients by facilitating thought leadership, support, information, and guidance in conjunction with sales partners
- Maintain strong working relationships with relevant Optiv technology partners, based on client spend, and Optiv focus in area of CoE focus and AI
- Identify and drive complete security programs to meet client objectives across technology and services including;
+ leading new discussions by leveraging peer and industry network contacts
+ performing requirements analysis, and technology selection criteria
+ coordinating demonstrations and security technology evaluations
+ leading cross organizational solutions leveraging Optiv's portfolio
- Interface and partner with the internal Optiv teams to align client expectations as it pertains to AI with the entire Optiv solution portfolio to ensure technology and service implementation and delivery excellence and client satisfaction
- Identify new and emerging AI technologies and products for internal enablement and exposure to clients
- Promote Optiv’s portfolio and security awareness at speaking events, partner events, writing industry articles and leveraging social media
- Maintains a reputation as trusted advisor with clients, partners, peers and cyber community resulting in an influential network of contacts
- Listen for client feedback and continually share with internal teams to evaluate and cultivate continuous improvement
- Participate in account planning, forecasting, and pipeline management activities as it pertains to growing Optiv pipeline, closed business, and industry mindshare as it pertrains to AI
- Participate in managing and prioritizing the proposal process to create business proposals, contracts, and response to RFI/RFP’s
- Actively pursue personal development by maintaining and obtaining technical capabilities, soft skills, and security specific knowledge through formal education, certification, and other avenues
- Expert sales techniques; makes connections, facilitates meetings, reads the room, asks probing questions, overcomes objections, gains trust, maintains composure under pressure, positions solutions, and assist in finalization of sale
What we're looking for
- BS/BA or equivalent and applicable work experience
- Minimum of ten (10\) years in an information security role, preferably as a consulting advisor, architect, team leader, director and/or higher
- Highly motivated self\-starter that does not require day\-to\-day management
- Ability to work in a highly adaptable and nimble team environment with responsive communication
- Thorough understanding of the current threat landscape, vulnerabilities, and defensive controls as it pertains to AI
- Strong business and technical acumen and ability to lead technology focused discussions as it pertains to AI
- Strong presentation, written, and oral communication skills to clients, including whiteboard sessions and other presentation mechanisms
- Strong attention to detail for reviewing statements of work (SOWs), quotes, and client deliverables
- Expands and shares broad security related knowledge and continuously expands their expertise in other domains across the portfolio as it pertains to a holistic approach to AI
- Vendor specific certification(s) focused primarily on AI and related areas of CoE focus
- Perform additional duties as directed or needed
- Ability to work in\-person and remotely with distributed teams in a decentralized environment
- Valid driver’s license
- Ability to travel within US and Canada, as well as internationally as needed
- CISSP, GIAC, CISA, CISM, CCSP or other relevant professional cybersecurity certifications that may relate to AI desired
- Experience speaking and evangelizing technology and AI thought leadership and vision at events
- Previous experience leading an information security function or program
- Organizational membership and participation in chapter meetings such as ISACA, ISSA
What you can expect from Optiv
- A company committed to our inclusive value through our Employee Resource Groups
- Work/life balance
- Professional training resources
- Creative problem\-solving and the ability to tackle unique, complex projects
- Volunteer Opportunities. “Optiv Chips In” encourages employees to volunteer and engage with their teams and communities.
- The ability and technology necessary to productively work remotely/from home (where applicable)
EEO Statement
Optiv is an equal opportunity employer. All qualified applicants for employment will be considered without regard to race, color, religion, sex, gender identity or expression, sexual orientation, pregnancy, age 40 and over, marital status, genetic information, national origin, status as an individual with a disability, military or veteran status, or any other basis protected by federal, state, or local law.
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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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Optiv, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Optiv AI Hiring
Optiv has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Leawood, KS, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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