Interested in this AI/ML Engineer role at ReSource Pro?
Apply Now →About This Role
*Do you thrive in client\-facing roles, building scalable solutions and driving long\-term growth? Are you motivated by the challenge of aligning finance with long\-term business vision?*
Come Join ReSource Pro!
Your Role...
ReSource Pro is seeking a Principal AI Solutions Architect, to design, validate, and define AI\-driven solutions that transform client operations. This highly autonomous role operates at the intersection of client engagement, AI product design, and business strategy, translating complex insurance workflows into scalable AI\-enabled solutions. You will connect with insurance clients and cross\-functional leadership to bridge the gap between conceptual ideas and production\-ready specifications.
We hire the best because we believe great people create exceptional experiences. That’s why we hire individuals who not only bring talent and passion, but who thrive in our unique culture and live out our Core Values: Commitment to Community, Teamwork, Passion for Excellence, Service\-Centric, and Best Self.
*All remote positions are based in the United States, and candidates must reside within the U.S. to be eligible for consideration.*
In This Role, You Will…* Lead structured discovery sessions with clients to assess workflows and technology environments.
- Architect end\-to\-end solutions across people, process, and technology components.
- Develop prototypes independently using AI/ML tools, LLM APIs, and low\-code platforms.
- Design and execute proof\-of\-concept models to demonstrate business value and feasibility.
- Translate validated use cases into scalable APM modules and detailed production specifications.
- Serve as a primary intake point for AI\-related opportunities across the organization.
- Partner with AI Factory engineering teams to ensure clarity of requirements during handoffs.
- Define success criteria, performance expectations, and technical constraints for AI solutions.
- Collaborate with Sales and leadership to evaluate the feasibility and fit of new opportunities.
- Monitor the alignment between designed solutions and delivered outcomes to support continuous improvement.
What You Need to be Successful…* Bachelor’s degree in Computer Science, Engineering, Business, or a related field (Master’s preferred).
- 8–12\+ years of experience in solutions architecture, product strategy, or technical consulting.
- Hands\-on experience with AI/ML technologies, Large Language Models (LLMs), and automation.
- Demonstrated success in client\-facing solution design and end\-to\-end architecture.
- Proven ability to independently prototype and validate complex technical solutions.
- Strong stakeholder management skills with the ability to influence cross\-functional teams.
- Deep understanding of insurance industry workflows or related complex business environments.
- High level of autonomy and comfort navigating ambiguity in an innovation\-focused role.
Your Compensation…
Our salary ranges are based on paying competitively for our size and industry, and are one part of the total compensation package that also includes annual bonus eligibility, benefits, and other opportunities at ReSource Pro. Individual pay decisions are based on a number of factors, including qualifications for the role, experience level, skillset, geography, and balancing internal equity relative to other ReSource Pro employees. This is a remote position, and the salary range for most locations for this role is $120,948 \- $205,026\. The salary range may vary based on experience and on the specific geographic location in which the candidate resides.
Your Benefits \& Perks…* 100% paid employee health insurance available on Day 1
- Eligible for all medical, dental, and vision benefits on Day 1
- Remote positions are Internet stipend\-eligible
- 401k with employer match, vested on Day 1
- HSA/FSA available
- Long Term and short\-term disability employer\-provided
- Generous PTO plan with paid holidays \+ floating holidays
- Development and growth opportunities
- Comprehensive wellness program and prioritization of employee health
Your Interview Process…
To be considered for this position, please submit your application. If you meet the qualifications for the role, a member of our Talent Acquisition team will be in touch to schedule an interview via Zoom.
The standard interview process includes:
- Behavioral interview with Talent Acquisition
- Online talent assessment
- Hiring Manager interview
- Stakeholder Interview
- Additional interview steps may be added depending on the position or if further evaluation is needed. Disclosure: Candidates are evaluated at each step of the process. As a result, not every candidate will complete all steps in the process.
About ReSource Pro:
Focused exclusively on the insurance industry, ReSource Pro is the trusted partner insurance organizations rely on to optimize performance, streamline operations and process engineering, and drive growth. Serving 2,000\+ carriers, brokers, wholesalers, and MGAs, ReSource Pro is a recognized market leader in insurance workflow optimization, data and technology services, and strategic operating model transformation. Maintaining a 96%\+ client retention rate for over a decade, ReSource Pro is the only firm serving the insurance industry to have earned a spot on the Inc. 5000 list 16 times—placing it among the top 0\.02% of repeat honorees across all sectors in the Inc. list’s 40\+ year history.
Equal Employment Opportunity Policy
ReSource Pro provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
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Salary Context
This $120K-$205K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ReSource Pro, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 24% below the category median. Disclosed range: $120K to $205K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ReSource Pro AI Hiring
ReSource Pro has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $205K - $205K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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