Interested in this AI/ML Engineer role at Optiv?
Apply Now →About This Role
*This position will be fully remote and can be hired anywhere in the continental U.S.*
The Vice President, Agentic Managed Detection \& Response (MDR) is a premier executive leader responsible for driving the overarching strategy, financial growth, and operational excellence of Optiv’s flagship security operations practice. This role is critical in scaling a high\-performing, multi\-million\-dollar, revenue\-generating organization while ensuring world\-class client satisfaction and industry\-leading security outcomes.
As a core member of the executive leadership team, the VP will define and execute the vision for next\-generation Detection \& Response. This requires an executive who blends deep commercial acumen with profound technical expertise—specifically leveraging modern data engineering, SIEM platform modernization, agentic AI architectures, and strategic technology partner ecosystems to optimize service delivery, maximize profitability, and accelerate market expansion.
How you’ll make an impact
Strategic Leadership \& Business Growth
- Define \& Execute Vision: Shape the strategic roadmap for the MDR and Advanced Fusion Center (AFC) practices, aligning modern security offerings with Optiv’s broader enterprise business objectives.
- Partner Alliance \& GTM Strategy: Partner with premier cybersecurity technology vendors, hyperscalers, and alliance partners to co\-develop joint go\-to\-market (GTM) strategies, capitalize on channel\-led revenue, and capture market share.
- P\&L \& Financial Accountability: Own complete P\&L responsibility for the practice, driving margin optimization, operational efficiency, and sustainable revenue growth.
- Market Differentiation: Drive continuous service innovation to position Optiv as a market leader, converting cutting\-edge technical capabilities and partner integrations into clear commercial advantages.
- Internal Cross\-Functional Alignment: Partner closely with senior executive peers, internal product management, and sales leadership to build unified cross\-functional growth strategies.
Operational Excellence \& Next\-Gen Service Delivery
- Co\-Innovation \& Partner Engineering: Collaborate deeply with key technology partners to influence their product roadmaps, ensuring seamless API integrations, robust data pipelines, and shared engineering initiatives that enhance Optiv’s proprietary MDR capabilities.
- Agentic SOC Architecture: Champion the transition from legacy workflows to autonomous, AI\-driven operations—specifically leveraging advanced orchestration, LLM platforms, and agentic capabilities to achieve drastic reductions in Mean Time to Detect/Respond (MTTR).
- Data Engineering \& SIEM Modernization: Oversee the strategy for large\-scale data ingestion, pipeline engineering, and optimization across diverse, partner\-backed enterprise SIEM platforms to maximize threat visibility and reduce backend operational costs.
- Global Security Operations: Ensure flawless, 24/7/365 end\-to\-end delivery of managed security services across global enterprise client environments, maintaining strict adherence to SLAs and compliance frameworks.
Executive Stakeholder Management \& Client Engagement
- Executive Sponsorship: Serve as the high\-impact executive sponsor for key strategic accounts and elite partner relationships, cultivating long\-term trust, ensuring retention, and identifying opportunities for account expansion.
- Thought Leadership: Lead executive briefings, CISO roundtables, and partner\-facing advisory boards to evangelize Optiv’s operational philosophy and vision for the future of managed security.
- Crisis \& Incident Escalation: Act as the ultimate technical and operational escalation point for critical, high\-severity security incidents, providing steady executive leadership during high\-stakes client crises.
Talent Leadership \& Succession Planning
- Globally Distributed Teams: Cultivate a customer\-obsessed, high\-performing culture across a globally distributed team of security engineers, data architects, and analysts.
- Succession \& Pipeline Development: Proactively lead workforce planning, mentorship, and succession strategies to identify, groom, and transition future leadership talent within the practice.
- Inclusive Excellence: Spearhead initiatives aimed at attracting, developing, and retaining top\-tier, diverse cybersecurity and engineering talent in a highly competitive market.
What we’re looking for
- Experience: 15\+ years of progressive leadership experience in enterprise cybersecurity operations, Managed Security Services (MSS/MDR), or premier security consulting.
- Education: Bachelor’s degree required; Master’s degree (MBA, MS in Cybersecurity, Computer Science, or Data Engineering) preferred.
- Certifications: Active Certified Information Systems Security Professional (CISSP) or Certified Information Security Manager (CISM) designation required.
- Partner Ecosystem Expertise: Proven track record of successfully navigating, influencing, and scaling strategic partnerships with major cybersecurity technology vendors (e.g., EDR, SIEM, XDR) and cloud providers to drive mutual business value.
- Financial Acumen: Proven track record of managing multi\-million\-dollar P\&Ls, optimizing operational margins, and scaling enterprise service practices.
- Technical Sophistication: Deep, authoritative knowledge of modern SOC tools, SIEM architectures, data pipelining, and the practical implementation of AI/LLM frameworks within security orchestration.
- Global Scale: Demonstrated success managing large, multi\-tiered, globally distributed technical operations and engineering teams.
- C\-Suite Influence: Exceptional communication skills with a proven ability to negotiate, influence, and build strategic partnerships at the C\-suite, partner executive, and Board level.
\#LI\-TW1
\#LI\-Remote
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.
Optiv respects your privacy. By providing your information through this page or applying for a job at Optiv, you acknowledge that Optiv will collect, use, and process your information, which may include personal information and sensitive personal information, in connection with Optiv’s selection and recruitment activities. For additional details on how Optiv uses and protects your personal information in the application process, click here to view our Applicant Privacy Notice. If you sign up to receive notifications of job postings, you may unsubscribe at any time.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; 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 $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.