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About This Role
### Company Overview
WRS Health is a leading cloud\-based Electronic Health Record (EHR) and Revenue Cycle Management (RCM) company serving medical specialties across the United States. We are transforming healthcare technology by leveraging artificial intelligence, autonomous agents, and advanced analytics to automate sales, marketing, and clinical workflows.
We are seeking a highly technical Sales Engineer who can architect and build an AI\-powered, agentic sales pipeline that drives scalable revenue growth. This is a hands\-on engineering role for someone who enjoys designing intelligent systems that combine CRM automation, LLMs, cloud infrastructure, and data science.
### Job Purpose
The Sales Engineer will design, implement, and optimize an end\-to\-end autonomous sales ecosystem using modern AI technologies. This individual will engineer intelligent sales agents capable of prospecting, qualifying, nurturing, and assisting sales representatives throughout the customer lifecycle.
The ideal candidate has a strong software engineering background, experience with Large Language Models (LLMs), cloud architecture, CRM automation, and data\-driven decision making.
### Key Responsibilities
- Design and build an agentic AI sales pipeline capable of autonomous lead qualification, nurturing, and opportunity management.
- Develop AI agents that collaborate to perform tasks such as:
+ Lead research
+ Prospect scoring
+ Personalized outreach
+ Follow\-up automation
+ Meeting preparation
+ Proposal generation
+ CRM updates
- Engineer integrations between HubSpot (or similar CRM), AI services, marketing platforms, and internal systems.
- Build workflows utilizing modern orchestration frameworks and multi\-agent architectures.
- Design prompt engineering strategies and evaluation pipelines for production AI systems.
- Implement cloud\-native AI infrastructure using AWS.
- Develop dashboards and analytics that measure funnel performance, AI effectiveness, conversion rates, and revenue attribution.
- Work closely with Sales, Marketing, Product, and Executive leadership to optimize the entire revenue lifecycle.
- Evaluate and integrate emerging AI technologies into the sales organization.
- Continuously improve AI agents through experimentation, A/B testing, and performance monitoring.
### Qualifications
#### Education
- Bachelor's degree or higher in:
+ Engineering
+ Computer Science
+ Data Science
+ Artificial Intelligence
#### Required Experience
- 5\+ years building enterprise software or AI solutions
- Experience designing agentic AI systems or multi\-agent workflows
- Proven success building automated sales or marketing systems
- Experience with HubSpot CRM or comparable enterprise CRM platforms (Salesforce, Microsoft Dynamics, Zoho CRM, etc.)
- Strong experience integrating APIs and SaaS platforms
- Experience implementing AI\-driven workflow automation
- Strong understanding of modern sales operations and pipeline management
Technical Skills:
#### Artificial Intelligence
- Experience with modern Large Language Models including:
+ OpenAI
+ Anthropic Claude
+ Google Gemini
- Prompt engineering
- Retrieval\-Augmented Generation (RAG)
- AI evaluation and testing
- AI orchestration frameworks
- Agent memory and planning architectures
#### Cloud \& Infrastructure
- AWS, Serverless architecture, API Gateway
#### Programming
- Python, JavaScript, TypeScript, SQL, REST APIs, Git, and CI/CD pipelines
#### Data \& Analytics
- Data modeling
- Predictive analytics
- Lead scoring
- Business intelligence
- Dashboard development
- Marketing attribution
- Sales forecasting
#### CRM \& Automation
- HubSpot
- CRM workflow automation
- Marketing automation
- Customer segmentation
#### Preferred Qualifications
- Experience in healthcare technology, EHR, SaaS, or Revenue Cycle Management
- Experience building AI copilots or autonomous business agents
- Familiarity with vector databases and semantic search
- Knowledge of healthcare interoperability standards and APIs
- Experience integrating conversational AI into customer engagement workflows
- Understanding of MLOps and production AI deployment
### What Success Looks Like
Within the first 12 months, you will:
- Build an autonomous AI\-powered sales pipeline
- Deploy intelligent agents that automate repetitive sales tasks
- Increase qualified lead generation through AI\-driven prospecting
- Improve CRM data quality and automation
- Reduce manual sales operations through intelligent workflows
- Deliver measurable improvements in pipeline velocity, conversion rates, and revenue growth
### Ideal Candidate
You are an engineer first and a problem solver at heart. You enjoy building intelligent systems that automate complex business processes and are excited by the opportunity to transform traditional sales organizations into AI\-powered revenue engines. You thrive in a fast\-paced environment where experimentation, innovation, and measurable results drive success.
### Additional Details
- Job Type: Full\-time
- Location: US \| Remote/Onsite/Hybrid
- Hours: Must be available during standard US business hours (9:00 AM – 5:00 PM EST or 8:30 AM – 4:30 PM EST)
This job description outlines the general responsibilities of the role and is not exhaustive. Additional duties may be assigned as needed to support business operations.
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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 WRS Health, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
WRS Health AI Hiring
WRS Health 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 $185,334 across 717 positions. About 14% 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 3,708 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
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