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About This Role
Requisition Number: 105833
Business Development Manager \- NetApp AI Infrastructure Solutions
Location:
You will have the flexibility to work fully remotely.
Insight at a Glance
- 14,000\+ engaged teammates globally
- $8\.2 billion in revenue in 2025
- Certified as a Great Place to work in 9 Countries in 2025
- Fortune 500 Company (No. 447\) in 2025
- Received 25\+ industry and partner awards in the past year
- $1\.4M\+ total charitable contributions in 2024 by Insight globally
Now is the time to bring your expertise to Insight. We are not just a tech company; we are a people\-first company. We believe that by unlocking the power of people and technology, we can accelerate transformation and achieve extraordinary results. As a Fortune 500 Solutions Integrator with deep expertise in cloud, data, AI, cybersecurity, and intelligent edge, we guide organizations through complex digital decisions.
POSITION OVERVIEW
The Business Development Manager (BDM) for NetApp AI Infrastructure Solutions is a cross\-functional, strategic growth role responsible for identifying, developing, and scaling AI infrastructure opportunities across all Insight business segments (Commercial, Enterprise, Public Sector/IPS, and Solutions). This role serves as a force multiplier for NetApp's AI\-ready storage portfolio, positioning Insight as an AI Solutions Integrator. The BDM bridges Insight's sales organization with NetApp's AI/ML product teams, driving adoption of NetApp's AI infrastructure capabilities, including AI Pod, AI\-optimized storage, and data management solutions for machine learning workloads. This role is critical to Insight's strategic initiative to 'Introduce IPS as AI Solutions Integrator' and to unlock new revenue streams in the emerging AI infrastructure market.
KEY RESPONSIBILITIES
- Drive execution of NetApp AI infrastructure business plan across all segments, identifying and developing AI\-specific use cases and target accounts
- Serve as subject matter expert on NetApp's AI\-ready storage portfolio, including AI Pod, AI\-optimized ONTAP, and AI data management solutions
- Identify and qualify net\-new AI infrastructure opportunities within target accounts across all business segments (Commercial, Enterprise, IPS, Solutions)
- Facilitate cross\-segment collaboration between Insight leadership, architects, and NetApp AI/ML field teams to develop AI infrastructure opportunities
- Design and execute field activities that generate AI infrastructure demand and pipeline (AI infrastructure workshops, proof\-of\-concepts, customer briefings, webinars)
- Act as a force multiplier for NetApp's AI product teams—amplifying AI infrastructure go\-to\-market efforts, coordinating account planning, and extending NetApp's AI reach across Insight's organization
- Establish and maintain strategic relationships with NetApp AI/ML leadership, including Product Managers, Solutions Architects, and Channel Development Managers focused on AI
- Design and deliver quarterly NetApp AI infrastructure enablement sessions for Insight teams across all segments, including solution training, use case development, and certification guidance
- Develop and communicate regular updates to NetApp on Insight's AI infrastructure pipeline, opportunities, and market intelligence across all segments
- Partner with Insight's Solutions team to develop AI infrastructure solution offerings and go\-to\-market strategies that leverage NetApp's AI capabilities
INTERNAL RESPONSIBILITIES
- Collaborate with sales and business leadership across all segments (Commercial, Enterprise, IPS, Solutions) to identify and prioritize AI infrastructure opportunities
- Work with Insight's organization to understand AI infrastructure pipeline, forecast, and resource constraints across all segments
- Facilitate joint planning sessions between Insight leadership and NetApp AI/ML field teams to align on AI infrastructure account strategies
- Communicate NetApp AI infrastructure contests, SPIFFs, promotions, and new product launches across the organization
- Track and report on AI infrastructure pipeline growth, deal registration, training participation, and engagement cadence across all segments
- Support Insight's marketing and Solutions teams in developing AI infrastructure campaigns, collateral, and demand generation initiatives
- Coordinate with Insight's architect community to develop AI infrastructure solution architectures and reference implementations
EXTERNAL RESPONSIBILITIES
- Facilitate quarterly business reviews (QBRs) and leader\-to\-leader cadences with NetApp AI/ML leadership to review AI infrastructure performance, align on strategy, and address escalations
- Serve as primary point of contact for NetApp's AI/ML product teams and field leadership regarding Insight's AI infrastructure business
- Coordinate with NetApp's Partner Sphere Program team to ensure Insight maintains compliance with partner program requirements and maximizes AI\-specific partner benefits
- Participate in NetApp AI infrastructure partner events, training sessions, and certification programs to stay current on AI product roadmaps and solutions
- Provide NetApp with regular intelligence on Insight's AI infrastructure market opportunities, customer feedback, and competitive dynamics across all segments
- Support NetApp's AI/ML field teams as a resource extension, including participation in customer meetings, technical discussions, and opportunity development
- Collaborate with NetApp's hyperscaler teams (Azure, AWS, GCP) to align AI infrastructure capabilities with cloud\-native AI platforms
REQUIRED KNOWLEDGE \& COMPETENCIES
- Deep understanding of NetApp's AI infrastructure portfolio, including AI Pod, AI\-optimized ONTAP, NetApp ONTAP AI, and AI data management solutions
- Working knowledge of AI/ML workload requirements, including data pipeline architecture, storage performance requirements, and AI infrastructure best practices
- Fluency in NetApp's hyperscaler AI integrations (Azure Machine Learning, AWS SageMaker, Google Cloud AI Platform) and ability to position NetApp AI storage in cloud\-native AI environments
- Understanding of the NetApp Partner Sphere Program and AI\-specific partner incentives and deal registration processes
- Strong knowledge of Insight's sales organization across all segments (Commercial, Enterprise, IPS, Solutions) and organizational structure
- Ability to identify and qualify AI infrastructure opportunities and develop AI use cases within target accounts
- Proficiency with CRM systems (Salesforce), quoting tools, and sales enablement platforms
- Excellent communication, presentation, and relationship\-building skills with technical and business audiences
- Ability to work cross\-functionally across multiple segments and influence without direct authority
- Strategic thinking combined with strong execution and attention to detail
- Understanding of emerging AI infrastructure market trends and competitive landscape
KEY PERFORMANCE INDICATORS (KPIs)
- AI infrastructure revenue and gross profit growth (year\-over\-year and vs. plan)
- AI infrastructure pipeline growth (new pipeline created and influenced)
- Net new AI infrastructure client acquisition (number of new accounts and associated revenue)
- AI infrastructure client expansion revenue (growth within existing accounts)
- AI infrastructure deal registration uplift and partner program compliance
- Quarterly Business Review (QBR) participation rate and engagement quality with NetApp AI/ML leadership
- AI infrastructure training sessions delivered and participation rates across all segments
- AI infrastructure use cases developed and qualified
- Partner engagement score and relationship health (NetApp AI/ML team feedback)
- Cross\-segment collaboration metrics (deals influenced across multiple segments)
QUALIFICATIONS
- Bachelor's degree in Business, Sales, Marketing, Computer Science, or related field (or equivalent professional experience)
- 5\+ years of experience in sales, business development, partner management, or account management in the technology industry
- Proven track record of driving revenue growth and building strategic partnerships in infrastructure or emerging technology segments
- Experience with AI/ML infrastructure, data management, or enterprise storage solutions preferred
- Experience in a partner\-facing or channel management role preferred
- Strong understanding of sales processes, pipeline development, and forecasting
- Excellent organizational and project management skills
- Ability to work effectively across multiple business segments and organizational structures
TRAVEL EXPECTATIONS
This position requires 35% travel for customer meetings, partner events, cross\-segment collaboration, internal business activities, and quarterly business reviews with NetApp AI/ML leadership. Travel includes visits to key AI infrastructure accounts, regional sales offices, NetApp partner events, and industry AI/ML conferences.
REPORTING STRUCTURE
This position reports to the Chief Revenue Officer or VP of Strategic Partnerships. The BDM works collaboratively with sales and business leadership across all segments (Commercial, Enterprise, IPS, Solutions), Insight's architect community, NetApp's AI/ML leadership, and cross\-functional teams across Insight. While aligned to support all business segments, this role does not have direct reporting relationships to individual segment leaders.
The position described above provides a summary of some the job duties required and what it would be like to work at Insight. For a comprehensive list of physical demands and work environment for this position, click here.
Insight is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.
Posting Notes: AZ\-Home \|\| Arizona (US\-AZ) \|\| United States (US) \|\| Project Management \|\| None \|\| Remote \|\|
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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Insight, 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 Required
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 $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Insight AI Hiring
Insight has 4 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Phoenix, AZ, US, Nashville, TN, US, TN, US.
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 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 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).
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 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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