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Technical Product Manager, Agentic AI Security
The Dell Security & Resiliency organization manages the security risk across all aspects of Dell’s business. We are currently experiencing incredible growth to meet the security needs of the world’s largest technology company. With team members located in over 15 countries, you will have an excellent opportunity to influence the security culture at Dell and further develop your career.
Join us as a Technical Product Manager, Agentic AI on our Dell Security & Resiliency Team in Round Rock, TX to do the best work of your career and make a profound social impact
What you'll achieve:
We are seeking a Principal Technical Product Manager to hold the specification for security workflows being delivered as AI-native capabilities. This role owns what gets built and why: the requirements, the measurable outcomes, the acceptance criteria, the scope, and the standard a workflow must meet before it is ready to move into design and build.
The specification is the contract between the people whose process is changing and the engineers changing it. This role works with domain functional experts to establish what the work is, how it is performed today, and what a correct outcome looks like, and works alongside engineering to keep the specification buildable as design proceeds.
Specifications are expected to be written, verifiable, and current. A requirement that cannot be verified, an outcome that cannot be measured, or a scope boundary that has not been stated is not complete.
Objectives of This Role
- A written specification for every workflow entering delivery, stating the requirements, the scope, and the outcomes the capability must produce
- Acceptance criteria that state what the capability must do, expressed so that meeting them can be verified rather than debated
- Measurable outcomes agreed with the functional owner before work starts, and evidenced after release
- Scope held stable through delivery, with changes made deliberately and their cost stated
You will:* Own the specification for each workflow, covering the problem being solved, the requirements, the scope boundaries, the constraints, and the outcomes the capability must produce
- Work with domain functional experts to establish how the work is performed today, what the process exists to achieve, where it varies, and what a correct result looks like, including the practice that has never been written down
- Define acceptance criteria that state what the capability must do to be accepted, expressed in terms that can be verified rather than interpreted
- Define the measurable outcomes for each workflow, including the baseline they are measured against and the improvement expected
- Own the definition of ready: the standard a specification must meet before a workflow moves into design and build, and hold specifications to that standard rather than allowing incomplete work forward
- Scope the work: separate what is required from what is desirable, state explicitly what is out of scope, and decompose large workflows into increments that can be delivered and used
- Resolve conflicting requirements between functions rather than accumulating them into the specification, and record the decision and the reasoning behind it
- Keep the specification current as design and delivery proceed, managing change deliberately and stating the cost of each change rather than absorbing it
- Work alongside engineering through design and build so that specifications remain buildable, and revise the specification when a technical constraint makes the original requirement impractical
- Validate delivered capability against the specification with the functional owner, and confirm after release whether the stated outcomes were achieved
- Maintain the specification standard, templates, and reference examples so that each workflow after the first takes less effort to specify
Take the first step toward your dream career Every Dell Technologies team member brings something unique to the table. Here's what we're looking for in this role:Essential Requirements
- 8+ years in technical product management, product ownership, or business analysis for software systems, with specifications that engineering teams built directly from
- Demonstrated ability to write requirements and acceptance criteria that are unambiguous and verifiable, and that do not require the author present to interpret them
- Experience eliciting requirements from domain experts, including surfacing the tacit practice that experienced people apply without documenting
- Experience defining measurable outcomes, including establishing a baseline and agreeing in advance what improvement will be measured and how
- Experience scoping and decomposing large bodies of work into deliverable increments, and holding scope through delivery under pressure to expand it
- Technical fluency sufficient to work with engineers on feasibility, constraints, and trade-offs, and to understand the systems being specified
- Working knowledge of AI-native systems, including why their behavior is probabilistic and what that means for how requirements and acceptance criteria must be written
- Demonstrated ability to resolve conflicting requirements between stakeholders and hold that decision
- Written communication of a standard that engineering can build from without repeated clarification
- Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience
Desired Requirements
- Experience in product management or business analysis within a security domain
- Experience specifying AI or machine learning capabilities for production use
- Experience with process analysis or process re-engineering, including documenting current state and defining a target state
- Prior engineering or technical delivery background
- Experience establishing specification standards or templates where none existed
- Familiarity with LLM behavior, agentic systems, or AI evaluation practice
Salary Context
This $130K-$169K range is in the lower quartile for AI Product Manager roles in our dataset (median: $193K across 93 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 8,943 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Dell Technologies, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 32% below the category median. Disclosed range: $130K to $169K.
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.
Dell Technologies AI Hiring
Dell Technologies has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Hopkinton, MA, US, Round Rock, TX, US, New York, NY, US. Compensation range: $130K - $476K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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