Manager, Agentic Engineering

$217K - $281K Round Rock, TX, US Mid Level AI/ML Engineer

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About This Role

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Manager, Agentic Engineering

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 Manager, Agentic Engineering on our Dell Security & Resiliency Team in Round Rock, TX. to do the best work of your career and make a profound social impact

Objectives of This Role

  • Delivery commitments met by a team operating at a pace it can sustain
  • An engineering bar held through direct review of design and code
  • A stated direction for AI-native capability in the security organization, revised as the technology and the demand change
  • Engineers whose capability and scope grow measurably under this management

We are seeking a Manager to lead the engineering team that builds, evaluates, and operates AI-native security capabilities, and to set the direction for what that organization becomes. This role manages individual contributors across all three functions and carries delivery accountability for the program.

What you’ll achieve:

This is a technical management role. The manager reviews designs, reads code, and judges engineering decisions directly rather than through status reporting. Delivery accountability at this level depends on knowing whether an approach is sound before the schedule shows that it was not.

The role also holds the forward view: what AI-native capability the security organization will need, which of it should be built rather than adopted, and what the team must become to build it. That direction is expected to be written down, defended, and revised as the technology changes.

You will:

  • Manage, coach, and develop individual contributors across build, evaluation, and operations, including performance management, career development, and compensation decisions
  • Hire into a market where the skills are scarce and claims are inflated, including an interview process that separates production experience from familiarity
  • Set and hold the engineering bar through direct participation in design review, code review, and technical debate, engaging on the substance of the decision
  • Own delivery commitments for the program, including forecasting, allocating capacity across concurrent work, and reporting variance while it can still be acted on
  • Make the build, adopt, and defer decisions for capability the team is asked to deliver, and state the reasoning behind each
  • Resolve trade-offs between delivery speed and engineering quality explicitly, and state which is being traded, rather than leaving the schedule to decide by default
  • Remove obstacles that engineers cannot remove themselves, across organizational, platform, and vendor boundaries
  • Define and maintain the direction for AI-native capability in the security organization, covering what should be built, what should be adopted, and what should be retired
  • Track the AI landscape closely enough to make adoption decisions with stated reasoning, and to change direction when the technology invalidates a plan
  • Plan the organization the direction requires, including roles, skills, capacity, and the sequencing of hiring against committed work
  • Grow the technical range of the team through deliberate exposure to unfamiliar problems, and establish succession for knowledge that currently sits with one person
  • Represent the engineering direction to executive stakeholders, translating between engineering reality and executive expectation in both directions

Take the first step toward your dream career

Every Dell Technologies team member brings something unique to the table. Here's what we are looking for in this role: Essential Requirements

  • 10+ years in software engineering with a record of building and shipping production systems, including 3+ years managing engineers directly
  • Current technical depth. This role reviews designs and reads code, and is expected to hold a position in technical debate on the merits rather than defer to the team on every judgment
  • Hands-on experience with AI-native system development, spanning agentic systems, LLM application engineering, evaluation of non-deterministic systems, or production operation of AI systems
  • Demonstrated delivery accountability: committed dates met, with a record of forecasting variance early rather than reporting it late
  • Experience hiring engineers in a competitive and immature talent market, and building an interview process that predicts performance
  • Demonstrated ability to develop engineers, with specific examples of people whose capability or scope grew under your management
  • Experience setting technical direction for a team or organization and revising it when circumstances changed
  • Executive communication: presenting direction, trade-offs, and risk to senior leadership in terms that support a decision
  • Working knowledge of security engineering sufficient to lead a team building security capability and to challenge its technical choices
  • Bachelor’s or master’s degree in computer science or a related field, or equivalent practical experience

Desirable Requirements

  • Experience managing engineers building AI or machine learning systems for production
  • Experience in a security engineering organization
  • Prior senior individual contributor role, at Principal level or above, before moving into management
  • Experience standing up a new engineering function rather than inheriting an established one
  • Experience managing a team through rapid technology change, including retiring work that a new capability made unnecessary
  • Familiarity with MCP, agentic frameworks, or AI evaluation practice

Salary Context

This $217K-$281K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Title Manager, Agentic Engineering
Location Round Rock, TX, US
Category AI/ML Engineer
Experience Mid Level
Salary $217K - $281K
Remote No

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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Dell Technologies, 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 (17% of roles) Aws (9% of roles) Azure (8% of roles) Rag (7% of roles) Gcp (5% of roles) Pytorch (5% of roles) Kubernetes (4% of roles) Claude (4% of roles)

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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($249K) sits 18% above the category median. Disclosed range: $217K to $281K.

Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; 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% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.

The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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

Based on 11,857 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $212,000. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 9,653 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Dell Technologies is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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