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About This Role
- Location: Denver, CO (hybrid — Tue/Wed/Thu in office)
- Reports to: CEO
The role
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We're hiring a Sales Product Specialist to run the technical sales motion at Ombud — the role that bridges product capability and customer outcome. You will own demos, proofs of concept, technical discovery, RFP and security questionnaire responses, sales enablement, and the demo environments and integrations that make our sales team credible in front of enterprise buyers.
The person who held this role previously came from an enterprise implementation background (Oracle ERP Cloud at KPMG) before moving into presales. That combination — knowing how enterprise software actually gets deployed, plus knowing how to sell it — is what makes this seat work. We are explicitly looking for the same profile.
What you'll own
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- End\-to\-end technical sales support: discovery, demos, custom builds, trials, proofs of concept.
- RFP and security questionnaire responses for active deals — Ombud's own platform is the tool you'll use, so you'll know it intimately.
- Demo environment design and maintenance: keep them current, relevant, and persuasive.
- Integration prototypes: HubSpot, Salesforce, Office365, Slack, Chrome extension, Excel add\-in, and customer\-specific connectors as deals require.
- Sales enablement: onboarding new AEs, training the sales team on product updates, building battlecards and competitive positioning against Loopio and Responsive.
- Executive and Board reporting on technical wins, POC outcomes, and presales metrics.
- Strategic renewals and upsells where technical depth is the deciding factor.
- Voice of the customer back into the product team — what's missing, what's broken, what's winning.
Must\-haves
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- 5\+ years of combined experience in enterprise software, with at least one tour in implementation/consulting (e.g., ERP, CRM, or SaaS implementation at a Big Four, systems integrator, or vendor professional services team) AND at least two years in presales / sales engineering.
- Detail\-oriented to the point of being noticeable. This role is where small errors become lost deals.
- Demonstrated ability to map complex enterprise business problems to technical solutions and articulate them to both technical buyers and executive economic buyers.
- Hands\-on fluency with generative AI tools (Claude, ChatGPT, Gemini, etc.) and familiarity with how LLMs and RAG architectures work at a conceptual level.
- Experience building and delivering technical demonstrations to enterprise audiences.
- Comfortable owning RFP and security questionnaire responses — directly, not as a coordinator.
- Willingness to be in\-office Tuesday through Thursday in Denver.
Nice\-to\-haves
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- APMP membership or active involvement in the Rocky Mountain Chapter.
- Prior experience in the response management, sales enablement, or revenue operations software category.
- Scripting or light development skills (Python, JavaScript, SQL) to build custom POC integrations without engineering support.
- Experience selling into or implementing for Workday, UKG, Salesforce, ServiceNow, or similar enterprise ecosystems.
What success looks like
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### *First 30 days*
- Complete product certification, shadow active deals, and inherit the existing demo library.
- Run your first three solo demos by the end of week four.
- Take ownership of one active POC.
### *First 60 days*
- Lead the technical workstream on at least two enterprise opportunities.
- Refresh demo environments with current product capabilities (Native engine, Editorial Workflows, self\-service).
- Build first competitive battlecard refresh.
### *First 90 days*
- Independently own the technical sales function across all active opportunities.
- Deliver first quarterly presales metrics report to the executive team.
- Onboard the new Commercial AE and any subsequent sales hires.
Why Ombud
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This is the seat where technical depth meets revenue impact. The person in this role is, in practice, the most influential individual contributor on the go\-to\-market team. You will sit at the intersection of every active enterprise deal and every product decision, and you will have direct line of sight to the CEO and the engineering organization.
### ABOUT OMBUD
Ombud is a Denver\-based B2B SaaS company building the agentic AI platform that powers Revenue Operations teams at enterprises like Workday, UKG, and Prudential. Our product, Ombuddy, automates the response work — RFPs, security questionnaires, proposals — that has historically eaten enterprise sales cycles. Our 2026 strategy is to extend this from response management into Orchestrated Revenue Operations: autonomous execution of the discrete sales processes that move revenue. Our 2035 BHAG is $1B ARR powering 80% of discrete B2B sales motions.
We run on EOS. We hire for output, not pedigree. We expect honesty over politeness, decisions over discussions, and execution over enthusiasm.
### HOW WE WORK — PIRCC VALUES
- Progressive — We grow. We learn. We push the model forward, not protect the status quo.
- Integrity — We do the right thing and keep our commitments. Said and done are the same thing.
- Resourceful — We turn constraints into creativity. We do more with less and bring solutions, not problems.
- Customer\-Centric — Our customers' success is the metric that matters. We anticipate their needs and earn their trust.
- Community — We build a team people want to be part of, and we invest in the communities we serve.
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 Ombud, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
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.
Ombud AI Hiring
Ombud has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US.
Location Context
AI roles in Denver pay a median of $184,000 across 159 tracked positions. That's 8% below the national 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 3,823 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
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