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Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world's biggest brands.
Director, Partner Solutions Engineering — Data & AI Security
About Veeam
Veeam is the Data and AI Trust company. We help organizations understand, secure, govern, and recover their data so they can accelerate AI safely and confidently. Headquartered in Seattle, with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide.
Join us as we move fearlessly growing, learning, and making major impact for some of the world's largest organizations.
The opportunity
Veeam is seeking a results-driven Director, Partner Solutions Engineering to lead a global team of partner solutions engineers who drive the technical adoption and integration of the Securiti AI portfolio. Working across Veeam's channel organization, the Director will build deep technical capability within regional and national partners, global system integrators (GSIs), solution providers, managed service providers (MSPs), advisory firms, and security-focused partners.
The Director will establish a consistent global strategy while giving regional teams the flexibility to respond to local markets, partner maturity, and customer needs. They will hire, manage, and develop a team of technical solution specialists while building a scalable technical partner motion that accelerates pipeline, technical wins, and customer adoption.
This role is ideal for a technical leader from a solutions engineering, sales engineering, or solution architecture background who combines deep expertise in data security and AI governance with strong executive influence, collaboration, accountability, and operational discipline.
What you'll do
Lead the global team and operating model
- Lead, coach, and develop a global team of approximately five to six Partner Solutions Engineers and technical solution specialists.
- Recruit, retain, and grow top technical talent, building clear career paths and technical skill development plans.
- Set priorities, performance expectations, operating cadence, and accountability across regions.
- Establish consistent standards for technical discovery, demonstrations, proofs of concept (POCs), solution design, technical enablement, and reporting on technical engagement and outcomes.
- Build a collaborative, accountable, and partner-focused team culture grounded in continuous technical learning, certification, and knowledge sharing.
Partner with Veeam's channel and field organizations
- Lead the Securiti AI technical specialist motion for partners, working closely with Veeam's channel organization and Strategic Partner Managers.
- Define and reinforce rules of engagement so Veeam Channel Managers retain ownership of the overall partner relationship, while the Partner Solutions Engineering team leads technical strategy, technical enablement, and presales support for Securiti AI opportunities.
- Equip channel and field teams with technical content, demo environments, reference architectures, and expert support needed to position and win with Securiti AI.
- Align sales, technical, marketing, services, operations, and leadership teams to remove obstacles and accelerate execution.
- Represent partners to Product Management and Engineering, sharing integration requirements, field insights, and competitive intelligence.
Drive partner technical strategy and solution architecture
- Define the global technical partner strategy across regional and national partners, GSIs, and global strategic alliances, in close alignment with partner management leadership.
- Prioritize technical investment in partners based on technical maturity, security expertise, services capability, geographic coverage, and growth potential.
- Own the technical components of joint business plans, including certification targets, solution development milestones, and technical readiness goals.
- Build trusted relationships with partner CTOs, technical leaders, and practice leads, and secure the technical resources needed to scale successful partnerships.
- Design repeatable, technically validated global and multi-region offerings, including assessments, workshops, implementation services, and managed services, supported by reference architectures and integration patterns partners can deploy at scale.
Build partner technical capabilities
- Lead technical enablement and certification programs for partner presales engineers, architects, and delivery consultants.
- Help partners build and scale practices in data security, Data Security Posture Management (DSPM), AI security, AI governance, privacy, and Governance, Risk, and Compliance (GRC).
- Provide technical discovery frameworks, demo scripts, lab environments, POC playbooks, competitive technical positioning, and customer use cases.
- Architect joint solutions and integrations that combine Securiti AI capabilities, partner services, and Veeam's broader portfolio.
Drive technical wins and adoption
- Accelerate partner-sourced and partner-influenced pipeline and revenue through technical discovery, demonstrations, POCs, workshops, assessments, and technical co-selling on priority opportunities.
- Align Veeam, Securiti AI, and partner technical teams around priority customers and opportunities, and engage directly on strategic deals.
- Lead regular reviews of technical engagements, POC outcomes, win/loss trends, and team performance.
- Use win/loss and engagement data to identify technical gaps, target enablement investment, and prioritize the highest-growth opportunities.
How success will be measured
- Technical win rate and conversion of partner-sourced and partner-influenced Securiti AI pipeline to revenue.
- Performance, development, and retention of the Partner Solutions Engineering team.
- Number of certified partner engineers and partners technically capable of selling, deploying, and delivering Securiti AI solutions.
- Growth in registered opportunities, new customers, and partner-led services.
- Delivery of technical commitments in joint business plans and alignment with Veeam's channel organization.
- Partner and field satisfaction with technical enablement and support, and return on enablement investments.
What you'll bring
- At least 10 years of experience in solutions engineering, sales engineering, solution architecture, or technical consulting in enterprise technology, including partner-facing or channel technical roles.
- Significant experience leading and developing teams of solutions engineers or technical specialists in a global, matrixed organization.
- A record of building technical partner programs that drive adoption and revenue across regional partners, national partners, GSIs, and global strategic alliances.
- Experience operating in a technical-overlay, specialist-presales, or strategic-alliance model.
- Hands-on depth in technical discovery, solution design, demonstrations, POCs, and technical enablement, with working knowledge of partner-led selling and pipeline management.
- Deep technical expertise in cybersecurity, data security, data governance, privacy, AI security, or a related enterprise technology market.
- The ability to translate technical capabilities into clear business outcomes, use cases, and partner sales plays.
- Executive presence and the ability to influence senior stakeholders without direct authority.
- Strong communication, relationship-building, and operational leadership skills.
- Willingness to travel internationally for partner and customer engagements, business reviews, enablement sessions, and industry events.
Preferred experience
- Direct experience with DSPM, data discovery and classification, AI governance, privacy management, or GRC platforms.
- Experience building technical enablement or certification programs for GSIs or multi-region partner ecosystems.
- Experience developing partner-led assessments, workshops, managed services, or professional services.
- Relevant certifications such as CIPP, CISSP, or a major cloud security credential.
Veeam Software is an equal opportunity employer and does not tolerate discrimination in any form on the basis of race, color, religion, gender, age, national origin, citizenship, disability, veteran status or any other classification protected by federal, state or local law. All your information will be kept confidential.
Personal data collected during the recruitment process will be processed in accordance with our Recruiting Privacy Notice, which explains how your information is collected, used, and handled in connection with hiring activities. By applying for this position, you consent to this processing.
By submitting your application, you confirm that the information provided, including any supporting documents, is complete and accurate to the best of your knowledge. Any misrepresentation, omission, or falsification may result in disqualification from consideration or, if discovered after employment begins, termination of employment.
Salary Context
This $292K-$543K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Veeam Software, 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 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($418K) sits 97% above the category median. Disclosed range: $292K to $543K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Veeam Software AI Hiring
Veeam Software has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $543K - $543K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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