Interested in this AI/ML Engineer role at Enlyte?
Apply Now →Skills & Technologies
About This Role
Company Overview:
At Enlyte, we combine innovative technology, clinical expertise, and human compassion to help people recover after workplace injuries or auto accidents. We support their journey back to health and wellness through our industry\-leading solutions and services. Whether you're supporting a Fortune 500 client or a local business, developing cutting\-edge technology, or providing clinical services you'll work alongside dedicated professionals who share your commitment to excellence and make a meaningful impact. Join us in fueling our mission to protect dreams and restore lives, while building your career in an environment that values collaboration, innovation, and personal growth. Be part of a team that makes a real difference.
Job Description :
The Vice President, Casualty AI Product Innovation is responsible for the strategic vision and technical execution of AI across our Payment Integrity Suite. This individual will lead the development of Generative AI (GenAI) and machine learning capabilities designed to transform and automate adjudication, enhance fraud/waste/abuse detection, and optimize adjacent cost containment services, including PPO, PBM, Specialty, and Clinical workflows.
Core Responsibilities
- Strategic AI Roadmap: Develop and execute a comprehensive multi\-year product strategy for AI and GenAI integration, prioritizing high\-impact opportunities within the Payment Integrity Suite and broader cost\-containment workflows.
- Technical Product Governance: Partner with the company’s AI COE to define the architectural standards for AI deployment ensuring high precision and auditability in regulated environments.
- Data Infrastructure Oversight: Partner with engineering and data science teams to build and maintain scalable data pipelines capable of ingesting, normalizing, and extracting value from high volumes of structured and unstructured medical and claims data.
- Operational Excellence: Lead the transition of AI capabilities from initial concept and proof\-of\-concept (POC) to enterprise\-scale production, with a focus on reliability, latency, and cost\-efficiency in high\-throughput bill processing environments.
Qualifications:
Technical Requirements
- Academic Foundation: A Bachelor’s or master’s degree in Operations Reserach, Industrial Engineering, Computer Science, Data Science, related quantitative field is required or equivalent experience.
- Engineering Background: Proven experience in software, process design, and/or data engineering prior to transitioning into product management; ability to engage deeply with architectural reviews.
- AI Specialization: Extensive recent experience with the Generative AI stack, including Large Language Models (LLMs), prompt engineering, and the deployment of agentic workflows.
- Data Expertise: Demonstrated success in building products reliant on complex data wrangling and high\-scale pipelines, particularly involving unstructured text and diverse data schemas found in medical billing.
Leadership \& Experience
- Executive Leadership: Minimum of 10 years of experience in technical product management, with at least 5 years in a senior leadership capacity.
- Strategic Execution: A track record of delivering enterprise\-grade AI products that have achieved measurable ROI and operational efficiency in financial or claims\-based systems.
- Regulated Industry Knowledge: Experience in Property \& Casualty (P\&C), Healthcare, or Fintech is highly preferred, with a thorough understanding of data privacy, compliance, and security standards (e.g., HIPAA, SOC2\).
Executive Competencies
- Analytical Rigor: Ability to assess technical feasibility against business constraints and market demands.
- Communication: Ability to articulate technical strategies to non\-technical stakeholders and external clients.
- Innovation Mindset: A proactive approach to identifying emerging technologies that can be leveraged to maintain a market\-leading position in payment accuracy.
Benefits:
This is a remote position that can be located anywhere in the US.
We’re committed to supporting your ultimate well\-being through our total compensation package offerings that support your health, wealth and self. These offerings include Medical, Dental, Vision, Health Savings Accounts / Flexible Spending Accounts, Life and AD\&D Insurance, 401(k), Tuition Reimbursement, and an array of resources that encourage a lifetime of healthier living. Benefits eligibility may differ depending on full\-time or part\-time status. Compensation depends on the applicable US geographic market. The expected base pay for this position ranges from $225,000 \- $250,000 annually plus bonus and equity, and will be based on a number of additional factors including skills, experience, and education.
*The Company is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, gender identity, sexual orientation, age, status as a protected veteran, among other things, or status as a qualified individual with disability.*
Don’t meet every single requirement? Studies have shown that women and underrepresented minorities are less likely to apply to jobs unless they meet every single qualification. We are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
\#LI\-PC1
\#LI\-Remote
Salary Context
This $225K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Enlyte, 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. This role's midpoint ($237K) sits 31% above the category median. Disclosed range: $225K to $250K.
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.
Enlyte AI Hiring
Enlyte has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
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.