Interested in this AI/ML Engineer role at Nintex?
Apply Now →Skills & Technologies
About This Role
About Nintex:
At Nintex, we are transforming the way people work, everywhere.
As the global standard for process intelligence and automation, we're trusted by over 10,000 public and private sector organizations across 90 countries. Our customers, from industry giants like Amazon, Coca\-Cola, and Microsoft, rely on the Nintex Platform to accelerate their digital transformation journeys by managing, automating, and optimizing business processes quickly and efficiently. We improve their lives through the technology we build.
We are committed to fostering a workplace that supports amazing people in doing their very best work every day. Collaboration is constant, our workplace is fun, the environment is fast\-paced, and we value our people's curiosity, ideas, and enthusiasm. Driven by passion and accountability, we take initiative, measure progress, and deliver results. Our culture fosters innovation and problem\-solving, fueled by curiosity and a commitment to thinking big. Together, we move with agility, prioritize customer needs, and build unity through empathy, leaving a positive impact wherever we go.
About the role:
The VP, Revenue Applied AI \& Data leads the strategy, architecture, and hands\-on execution of AI\-driven solutions across the Revenue organization.
This role owns the end\-to\-end applied AI function for Revenue — spanning use\-case identification, solution architecture, data foundations, model development, vendor strategy, and budget — and is accountable for delivering scalable, production\-ready AI capabilities that drive measurable business outcomes.
Operating with a lean, production\-first delivery model, the VP both sets direction and personal builds, ensuring AI moves from concept to deployed, monitored, value\-generating systems embedded in everyday Revenue workflows.
Your contribution will be:
- Architect, build, and deploy production\-grade AI agents and LLMpowered solutions embedded within daily internal Revenue workflows.
- Evaluate and execute build vs. buy vs. platform decisions — leveraging Nintex Automation CE and K2 where appropriate, partnering with Product and Engineering on shared infrastructure, and developing custom agentic systems using Claude and other foundation models for unique internal needs.
- Own the AI function's budget, vendor relationships, and technical roadmap for internal applications.
- Define, track, and report key performance indicators (KPIs) that link AI investments to measurable outcomes in efficiency, revenue generation, and cost optimization; present progress to leadership on a monthly basis.
- Establish clear release standards for AI initiatives, ensuring each deployment has defined outcomes, an accountable workflow owner, and production monitoring in place.
- Communicate Revenue AI strategy, investment rationale, and return on investment (ROI) to executive leadership — including the CEO, CFO, and Board — translating technical concepts into audience\-appropriate insights.
- Design and deploy AI\-driven automation across the full customer lifecycle, including pipeline qualification, proposal generation, onboarding, QBR preparation, renewal forecasting, and customer health scoring.
- Lead the automation of finance workflows, including reporting, variance analysis, contract analysis, audit preparation, and spend optimization.
- Identify and eliminate manual, high\-friction operational processes across the organization; enable scalable adoption through self\-service AI tools for business teams.
- Own and evolve the data engineering foundation, including data pipelines, warehouse architecture, data quality standards, and governed access layers.
- Develop and operationalize proprietary models trained on Nintex data (customer, product usage, and operational data) to improve retention, expansion forecasting, and business visibility.
- Establish and enforce governance frameworks for responsible, reliable, and scalable AI deployment, including model evaluation, monitoring, and data privacy and security standards.
- Operate with a lean, outcome\-focused delivery model, driving measurable impact through cross\-functional partnerships with business and technical teams.
- Define and evolve the organizational structure and reporting model for the AI function in partnership with leadership, aligning resources to areas of highest leverage.
- Set and reinforce a high\-performance culture emphasizing rapid iteration, production\-first delivery, and clear ownership with defined timelines for all initiatives.
To be successful, we think you need:
Education
- Bachelor's degree in Computer Science, Data Science, Engineering, or a related field (or equivalent practical experience).
Years of Experience
- 12\+ years building and deploying applied AI, machine learning, or data engineering systems in production environments, with 5\+ years leading high\-performing technical teams or functions.
Preferred Qualifications:
- Advanced degree (MS or PhD) in Computer Science, Machine Learning, Data Science, or a related discipline.
- Demonstrated experience designing and shipping LLM\-powered and agentic systems to production at scale (prompt orchestration, retrieval, evaluation, and monitoring).
- Background in mid\-market and/or private equity–backed SaaS environments, with an understanding of their operating cadence, growth expectations, and capital discipline.
- Hands\-on experience with GTM / Revenue systems (e.g., CRM, CPQ, customer success and billing platforms) and the data they generate.
- Experience with modern data platforms and tooling (e.g., cloud data warehouses/lakehouses, ELT/transformation, orchestration) and MLOps/LLMOps practices.
- Familiarity with workflow automation / iPaaS platforms such as Nintex Automation CE, K2, or comparable tools.
- Track record of partnering with Security, Legal, and Privacy functions to deploy AI responsibly and compliantly.
What's in it for you?
Nintex has a hybrid working model, enabling us to build culture, learn, and grow together. We intentionally connect and collaborate, while emphasizing flexibility with a blend of at\-home and in\-office work. This role is a hybrid role in our local Nintex office.
While our offerings differ from country to country, we offer our entire global workforce an array of exciting perks and benefits, including
- Global Gratitude and Recharge Days
- Flexible, paid time off policy
- Employee wellness programs and counseling resources
- Meaningful peer recognition and awards
- Paid parental leave
- Invention/patenting assistance
- Community impact, paid volunteer time, and opportunities
- Intercultural learning and celebration
- Multiple tools through which to learn and grow, and an incredible global community
View more about our benefits here: https://www.nintex.com/wp\-content/uploads/2023/01/Global\-Perks\-and\-Benefits.pdf.
Target Compensation Range (US ONLY): 275,000 \- 375,000 USD annually. On target compensation refers to the base salary and applicable variable target for this role. The range is an estimate, base pay will ultimately be decided at the offer stage, based on an individual candidate's skills and experience aligned with the needs of the role. Base pay may vary based on several factors including geographic location, role specific qualifications, and seniority. Nintex also offers a competitive benefits package including paid time off, twelve paid holidays, 401(k) with employer match, and more.
*Nintex participates in E\-Verify for work authorization. We are an Equal Employment Opportunity Organization.*
Salary Context
This $275K-$375K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Nintex, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($325K) sits 49% above the category median. Disclosed range: $275K to $375K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Nintex AI Hiring
Nintex has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $375K - $375K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.