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About This Role
AI Development \& Agent Ops — Product Manager
Product Management — Infrastructure Solutions Group
Join us to do the best work of your career and make a profound social impact as a Product Manager on our AI Development \& Agent Ops team in Hopkinton, MA or Austin, TX,
The Opportunity
Dell Technologies is at an inflection point in how software is built. Our Infrastructure Solutions Group — home to one of the industry's largest developer organizations — is undergoing a fundamental transformation: from traditional SDLC to an agentic development model where engineers orchestrate AI agents across the full software lifecycle.
This newly created, hands\-on role sits at the center of a broad and evolving portfolio of agentic operations work — designing, building, and refining the services, platforms, and practices that will enable \~9,000 engineers to develop with agents at scale.
Why This Role
This is not a support role. Dell's AI Development \& Agent Ops organization is operating at the frontier of what it means to build software with AI — and this individual will be in the engine room making it work. You will partner directly with a VP who is deeply engaged in the work, contribute to shaping how one of the world's largest technology companies builds software, and operate at a scale where your individual contributions have outsized organizational impact.
You will also be joining at the right moment: early enough to shape the architecture of the transformation, with enough organizational commitment that the work has real resources and real stakes.
About Dell Technologies — ISG Developer Experience
Dell's Infrastructure Solutions Group is a global leader in Solutions, Storage, Compute, and Networking. The Developer Experience organization within ISG is responsible for developer productivity, tooling, observability, and platform engineering for approximately 9,000 engineers. Our current mandate spans agentic SDLC transformation, AI tooling governance, and the operating model changes required to make AI\-assisted development the norm — not the exception.
Infrastructure Solutions Group (ISG) builds the products that power infrastructure, solutions, and data management our customers need most. Our teams design and develop the hardware and software that connect infrastructure, accelerate computational workloads, integrate across the stack, protect data continuity, and deliver the platforms, applications, and diagnostics our customers rely on every day at enterprise scale.
Some product builders follow the rules. Ours rewrite them.
Within ISG, innovation isn't just about creating new technology\- it's about challenging assumptions and reimagining how work gets done. Engineers define intent, author precise specifications, and orchestrate AI agents that execute at speed and scale. AI is embedded throughout our development process, helping us move faster, learn quicker, and deliver greater impact. Yet the most important contribution remains uniquely human: deciding what to build, designing how it should work, and applying the judgment needed to earn our customers' trust. When your products power critical infrastructure around the world, that responsibility matters.
We move quickly toward the work that matters most. We reward experimentation, encourage bold thinking, and remove unnecessary barriers so great ideas can become reality faster. Our teams embrace a customer\-first, first\-to\-market mindset, transforming rapid feedback into better products and better outcomes. We aren't looking for people who are content with the status quo\- we look for builders who question it, improve it, and occasionally rewrite it.
If you're energized by solving complex systems challenges, excited to work alongside AI to amplify your impact, and motivated by the opportunity to shape what's next instead of simply maintaining what's been done before, you'll find your place here.
Join us and help build the future of enterprise technology.
What You'll Achieve
As a Product Manager — AI Development \& Agent Ops, you will be responsible for shaping the agentic portfolio strategy and designing capabilities that enable \~9,000 engineers to develop with AI agents at scale. You will work with cross\-functional stakeholders across ISG on Dell's agentic SDLC transformation — driving how the organization scales and integrates agentic capabilities across the full software development lifecycle.
What We're Looking For
The right candidate is a versatile technical practitioner who combines deep fluency and domain expertise with a genuine passion for the emerging agentic AI landscape. We are looking for someone who is a builder — think prototypes, frameworks, dashboards, documentation — and who thrives in environments where the playbook is still being written.
You will:
- Influence the agentic portfolio strategy, roadmap, and prioritization framework, defining how Dell scales and integrates agentic capabilities across the SDLC to drive business and engineering outcomes
- Design and develop agentic capabilities that enhance Product Management across ISG, enabling more effective planning, requirements definition, decision\-making, and execution upstream in the SDLC
- Establish and govern specification standards, quality gates, and operational best practices to ensure consistent, scalable, and high\-quality adoption of agentic solutions across the SDLC
- Research emerging trends, technologies, and market developments within the AI and agentic landscape; conduct proofs of concept, pilots, evaluations, and experimentation to identify opportunities for innovation and value creation
- Manage and assess the agentic vendor and partner ecosystem, evaluating solution capabilities, market offerings, and strategic partnerships to strengthen Dell's AI portfolio
- Partner with cross\-functional stakeholders across ISG to align priorities, influence decision\-making, and build consensus on agentic strategy, investments, and portfolio direction
- Develop executive\-level recommendations, business cases, and portfolio insights that enable leadership alignment and accelerate adoption of agentic capabilities across the organization
Take the First Step Towards Your Dream Career
Every Dell Technologies team member brings something unique to the table. Here's what we are looking for with this role:
Essential Requirements
- 8\+ years in a product management role within software\-intensive organizations, with experience contributing to large\-scale transformation
- Deep hands\-on familiarity with modern software development practices — CI/CD, DevOps, developer tooling — and a strong working knowledge of the current AI/agentic development landscape
- Demonstrated ability to take ambiguous problems, structure them, and deliver concrete outputs: analyses, prototypes, specifications, or frameworks
- Experience working cross\-functionally with engineering, product, security, and infrastructure stakeholders
- Strong written and verbal communication skills — able to translate technical complexity into clear documentation for both technical and non\-technical audiences.
- Self\-directed work style with a track record of delivering high\-quality work with minimal oversight
Desirable Requirements
- Direct, hands\-on experience with agentic development tools and platforms (e.g., Windsurf/Codeium, Claude Code, GitHub Copilot, Cursor, or equivalent)
- Familiarity with MCP ecosystems, LLM token economics, and AI governance frameworks at enterprise scale
- Experience in a large enterprise environment with gated release cycles, compliance requirements, and non\-negotiable human oversight needs
- Background in developer experience, platform engineering, or internal developer tooling
- Experience building dashboards, measurement frameworks, or data instrumentation for developer productivity
- Comfort operating in ambiguity: defining structures and processes where few exist, and creating clarity without waiting for it
Compensation
Dell is committed to fair and equitable compensation practices. The salary range for this position is $175,200 to $240,900\.
Benefits and Perks of Working at Dell Technologies
Your life. Your health. Supported by your benefits. You can explore the overall benefits experience that awaits you as a Dell Technologies team member — right now at MyWellatDell.com
Who We Are
We believe that each of us has the power to make an impact. That’s why we put our team members at the center of everything we do. If you’re looking for an opportunity to grow your career with some of the best minds and most advanced tech in the industry, we’re looking for you.
Dell Technologies is a unique family of businesses that helps individuals and organizations transform how they work, live and play. Join us to build a future that works for everyone because Progress Takes All of Us.
Dell Technologies is committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. Read the full Equal Employment Opportunity Policy.
Visit our *Culture Code* page to learn more about how we work and lead.
Salary Context
This $175K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $175K to $240K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Dell Technologies AI Hiring
Dell Technologies has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager. Positions span Round Rock, TX, US, Austin, TX, US, Hopkinton, MA, US. Compensation range: $202K - $304K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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