Interested in this AI/ML Engineer role at Diebold Nixdorf?
Apply Now →About This Role
*Expect more. Connect more. Be more at Diebold Nixdorf. Our teams automate, digitize, and transform the way more than 75 million people around the globe bank and shop in this hyper\-connected, consumer\-centric world. Join us in connecting people to commerce in this vital, rewarding role.*
The Senior Director, AI and Data Platform will be responsible for shaping and leading IT’s enterprise capability in artificial intelligence, data platforms, and analytics. This is a new leadership position created to accelerate the organization’s ability to deploy AI responsibly and at scale, while also transforming the underlying data and analytics environment needed to support future business growth.
The role will provide IT leadership for AI, partnering with key stakeholders to identify, assess, prioritize, and deliver AI use cases that create measurable business value. This includes establishing the technical, architectural, engineering, and operational capabilities required to move from experimentation to production\-grade AI solutions. The role will lead the AI engineering effort, ensuring that AI solutions are robust, secure, scalable, maintainable, and aligned with technology standards.
The role will develop and operate the organization’s AI guardrails and governance model. The Senior Director will work across IT (including information security), legal, compliance, data privacy, risk, procurement, and business leadership to ensure AI is adopted safely, ethically, and in line with company policies. This will include defining decision rights, review processes, platform standards, reusable patterns, approved tools, data controls, and lifecycle management for AI solutions.
The role will also lead Data and Analytics for IT. This includes transforming the current data infrastructure, data operating model, analytics capabilities, and ways of working. The Senior Director will be accountable for developing a modern, scalable data platform strategy; improving data quality, accessibility, governance, and integration; and enabling stronger analytics, reporting, and self\-service insight capabilities across the organization.
The successful candidate will be a strategic technology leader with strong execution discipline. They will combine enterprise IT leadership experience with deep understanding of AI, data platforms, analytics, cloud technologies, governance, and engineering delivery. They must be able to operate at executive level while also providing practical direction to teams responsible for platforms, products, data pipelines, AI engineering, governance, and analytics enablement.
- Lead the Diebold Nixdorf IT strategy and roadmap for AI, data platforms, and analytics, aligned to business priorities and the CIO’s technology agenda.
- Partner with senior business and functional stakeholders to identify, evaluate, prioritize, and sequence AI use cases based on value, feasibility, risk, readiness, and strategic alignment.
- Establish and lead the IT capabilities required to deliver AI at scale, including AI engineering, solution architecture, platform enablement, model lifecycle practices, integration patterns, testing, monitoring, support, and continuous improvement.
- Lead the AI engineering function, ensuring AI solutions are designed and delivered using robust software engineering, data engineering, security, architecture, and operational practices.
- Develop, implement, and continuously improve Diebold Nixdorf AI guardrails, including standards for responsible AI use, data protection, security, human oversight, transparency, vendor use, model management, and production deployment.
- Lead AI governance across IT, working closely with cybersecurity, legal, compliance, risk, privacy, procurement, enterprise architecture, and business stakeholders to establish clear decision rights, review processes, and control points.
- Define and manage the approved AI technology ecosystem, including enterprise AI platforms, development tools, reusable components, integration patterns, and vendor solutions.
- Build and mature the organization’s AI delivery model, balancing experimentation, proof of concept activity, production delivery, reuse, risk management, and measurable business outcomes.
- Lead Data and Analytics for IT, including the transformation of existing data infrastructure, reporting platforms, analytics capabilities, and data ways of working.
- Develop and execute a modern data platform strategy that supports analytics, AI, automation, reporting, operational insight, and future digital capabilities.
- Improve enterprise data quality, accessibility, integration, lineage, metadata, governance, and lifecycle management in partnership with business data owners and technology teams.
- Establish stronger analytics capabilities across IT and the wider business, including self\-service analytics, executive dashboards, operational reporting, advanced analytics, and data product practices.
- Lead the transition from legacy or fragmented data environments toward more scalable, governed, cloud\-enabled, and product\-oriented data capabilities.
- Build, lead, coach, and develop a high\-performing multidisciplinary team across AI engineering, data engineering, platform management, analytics, and governance.
- Manage team priorities, capacity, performance, budget, vendor relationships, delivery plans, and operating metrics.
- Work closely with Enterprise Architecture to ensure AI, data, and analytics solutions align with enterprise architecture principles, technology standards, integration strategy, and long\-term platform direction.
- Partner with information security and infrastructure teams to ensure AI and data platforms are secure, resilient, compliant, observable, and operationally sustainable.
- Establish KPIs and value measures for AI, data, and analytics initiatives, including adoption, productivity, cost, quality, risk reduction, speed to delivery, and business impact.
- Communicate progress, risks, decisions, and recommendations clearly to the CIO, executive stakeholders, governance bodies, and cross\-functional leadership teams.
- Stay current with developments in AI, data platforms, analytics, cloud, automation, and responsible AI, translating relevant trends into practical enterprise opportunities.
Required Qualifications
- Significant experience in senior IT, data, analytics, AI, digital, or technology leadership roles within a complex enterprise environment.
- Demonstrated ability to lead enterprise\-scale AI, data platform, analytics, cloud, or digital transformation initiatives.
- Strong understanding of generative AI, machine learning, AI engineering, data engineering, analytics platforms, cloud data architectures, integration patterns, and enterprise technology delivery.
- Proven experience developing technology strategy, roadmaps, operating models, governance processes, and scalable delivery capabilities.
- Experience leading AI use case prioritization, AI delivery, responsible AI practices, or AI governance in an enterprise context.
- Strong understanding of data governance, data quality, metadata, data lineage, master data concepts, data integration, reporting, analytics, and self\-service business intelligence.
- Experience modernizing data infrastructure, including cloud\-based data platforms, data lakes, data warehouses, lakehouse architectures, or equivalent enterprise data environments.
- Strong people leadership experience, including building, managing, and developing multidisciplinary technical teams.
- Ability to influence and align senior stakeholders across business functions, IT, cybersecurity, legal, compliance, procurement, privacy, risk, and enterprise architecture.
- Strong commercial and vendor management capability, including experience assessing, selecting, implementing, and governing third\-party technology solutions.
- Excellent judgment in balancing innovation, speed, risk, governance, security, cost, and operational reliability.
- Strong executive communication skills, with the ability to explain complex AI, data, and technology topics in clear business terms.
- Experience operating in a global or matrixed organization is preferred.
- Experience in manufacturing, services, supply chain, or similarly complex operational environments is advantageous.
- Bachelor’s degree in computer science, engineering, data science, information systems, business, or a related discipline preferred; advanced degree or relevant certifications are advantageous.
- Based in the United States and able to work effectively in a remote leadership role, with periodic travel as required.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Diebold Nixdorf, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Diebold Nixdorf AI Hiring
Diebold Nixdorf has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.