AI Solution Engineer, Security

$212K - $275K Round Rock, TX, US Mid Level AI/ML Engineer

Interested in this AI/ML Engineer role at Dell Technologies?

Apply Now →

Skills & Technologies

ClaudeDrift AiJavascriptPythonTypescript

About This Role

AI job market dashboard showing open roles by category

AI Solution Engineer, 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 an AI Solution Engineer, Security 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

  • Unified AI-native workflows that replace duplicated processes across security functions, reducing complexity rather than automating it in place
  • Measurable compression of security cycle time, evidenced against a baseline of the process as it runs today
  • Security capability embedded in business and engineering workflows, exercised as part of the work rather than as a checkpoint applied to it What you'll achieve:

We are seeking a Senior Principal Engineer to serve as the design authority for how security work is reconstituted as AI-native capability. This is a senior individual contributor role with no direct reports and substantial technical authority: the solution designs, agent authority boundaries, and reference patterns produced by this engineer determine what gets built and to what standard.

Security work today is spread across many functions, each with its own intake path, tooling, and definition of done. The result is duplicated effort and process that runs at human speed while the systems it protects run at machine speed. This role works alongside functional owners and practitioners across security to collapse many processes into one, and to embed the resulting capabilities into the business and engineering processes they serve, so that security is intrinsic to how work is done rather than a gate applied to it.

You will:

  • Own the solution design for AI-native security workflows from problem statement through buildable specification: agent topology and decomposition, the boundary between model reasoning and deterministic logic, tool and data surfaces, state and context strategy, human decision points, and defined failure behavior
  • Determine what should be agentified, assessing whether the work is better served by an agent, by deterministic automation, or by leaving it unchanged, and document reasoning that withstands challenge
  • Collaborate with functional owners and practitioners across security to establish how work is actually performed, identify where separate functions perform the same work under different names, and design the single consolidated process that replaces them, including the shared data and evidence model that makes one process possible, resolving conflicting requirements rather than accommodating all of them
  • Define authority boundaries for every agent: what it is permitted to do, what requires human confirmation, what it must never do, and how those limits are enforced through scoped credentials and technical controls rather than by convention
  • Design against attacks on the agentic system itself: prompt injection, tool abuse, untrusted content reaching the context window, and data exfiltration through agent surfaces
  • Baseline current cycle time, manual touch points, and rework before designing the replacement, so improvement is measured rather than asserted, and define what is retired once the unified workflow lands
  • Design for verifiability and failure containment: the evidence and traces each workflow emits, deterministic criteria for what a passing outcome means, the blast radius of an incorrect agent action, and the rollback and escalation paths
  • Design how agentic workflows integrate with the systems security work runs on, including security tooling, systems of record, identity and access, data platforms, and the collaboration surfaces where work is requested and reviewed, using APIs, event streams, and protocols such as MCP (Model Context Protocol)
  • Establish the reusable foundation of reference architectures, agent primitives, orchestration and context patterns, evaluation harnesses, and integration templates, holding token cost, latency, and reliability as design constraints rather than post-launch concerns

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

  • 12+ years in software engineering, security engineering, or platform architecture, with a record of production delivery rather than advisory or research-only work
  • AI and agentic systems delivered into production and kept there, with measurable operational or business impact
  • Deep hands-on capability in agentic system engineering, including: Agent loop design: planner and executor patterns, sub-agent delegation, tool and function calling, state management, checkpointing and resumability across long-running workflows, and idempotency for agents that take consequential action - Context engineering: agentic retrieval and search, memory architecture, grounding in enterprise data, context compaction, tool and schema design, and structured or schema-constrained output - Determinism boundary design: placing model reasoning where judgment is genuinely required and deterministic logic where a verdict, gate, or calculation must be reproducible- Evaluation engineering: golden sets, offline and online evaluation, regression suites, acceptance thresholds, and the limits of model-graded evaluation - Production economics: token budgeting, prompt and response caching, model selection and routing with fallback, and latency and cost targets held as design constraints
  • Design of agent authorization and identity: non-human identity, scoped and least-privilege credentials, delegation, action approval, and audit trails that hold up under review
  • Ability to threat model an agentic system and design against prompt injection, tool abuse, untrusted content in the context window, and exfiltration through agent surfaces
  • Strong programming proficiency (Python, TypeScript/JavaScript, Go, or similar). This role designs, prototypes, and reviews code; it does not stop at diagrams
  • Distributed systems and integration engineering: API and event-driven design, schema and contract versioning, retry and backpressure behavior, and observability including trace-level visibility into agent execution
  • Data modeling across systems of record, including entity resolution and reconciliation of overlapping representations of the same asset, finding, or owner
  • Demonstrated experience leading cross-functional process consolidation, bringing together teams that hold separate processes, conflicting requirements, and existing tooling investments, and landing a single agreed design those teams adopt
  • Working knowledge of security engineering sufficient to design credible security workflows in application security, vulnerability management, threat modeling, secure SDLC, or an equivalent domain
  • Demonstrated influence without authority at senior levels, communicating equally well to engineers building from a design and executives deciding on it
  • Bachelor’s or master’s degree in computer science or a related field, or equivalent practical experience

Desired Requirements

  • Experience applying agentic AI within a security domain such as application security, DevSecOps, vulnerability management, threat modeling, or software supply-chain security
  • Experience building MCP servers, agent skills and tools, or extending AI coding assistants such as Claude Code, GitHub Copilot, or Cursor
  • Familiarity with secure-by-design and secure-by-default principles and relevant frameworks, including the OWASP Top 10 for LLM Applications, NIST SSDF, and NIST AI RMF
  • Experience with LLMOps in production, covering versioning, staged rollout, regression detection, and behavioral drift monitoring, and with automated processes that remain defensible under audit
  • Prior senior individual contributor role (Principal or above) carrying organization-wide design authority

Salary Context

This $212K-$275K 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 AI Solution Engineer, Security
Location Round Rock, TX, US
Category AI/ML Engineer
Experience Mid Level
Salary $212K - $275K
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 Required

Claude (4% of roles) Drift Ai (1% of roles) Javascript (2% of roles) Python (17% of roles) Typescript (3% 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 ($243K) sits 15% above the category median. Disclosed range: $212K to $275K.

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.

Get Weekly AI Career Intelligence

Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.