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 :
This is a full\-time remote position that can be located anywhere in the U.S.
The Director, AI Governance \& Portfolio is responsible for establishing and maturing the governance, portfolio management, and operating rhythms that enable responsible, scalable AI adoption across Enlyte. This leader will own the enterprise AI governance framework and the authoritative AI portfolio view, ensuring AI investments are intentional, compliant, measurable, and aligned to business outcomes.
This role will operate within Enlyte’s hybrid federated AI model, partnering closely with business\-unit AI Catalyst leads, Legal, Compliance, Information Security, Privacy, Finance, data science, engineering, and executive stakeholders. The position begins as a senior individual contributor with a clear trajectory to build and lead a dedicated function as governance and portfolio capabilities mature.
This role begins as a senior individual contributor with trajectory to build and lead a dedicated team as the function matures.
Core Responsibilities:
AI Governance
- Enterprise AI Governance: Mature Enlyte’s AI governance framework, including policies, standards, review processes, escalation pathways, and decision forums.
- AI Use Case Lifecycle Management: Evolve the AI use case intake, review, approval, and deployment lifecycle, including stage gates, risk classification, ownership, and accountability structures.
- Responsible AI \& Regulatory Readiness: Operationalize responsible AI principles in partnership with Legal, Compliance, Information Security, and Privacy, while translating evolving regulatory expectations into practical controls.
- Enterprise AI Portfolio Management: Maintain and mature a single authoritative view of AI and ML initiatives across business units, lifecycle stages, investment categories, risks, and business outcomes.
- Portfolio Health \& Investment Insights: Develop portfolio metrics and reporting that surface ROI, risk concentration, duplication, vendor sprawl, resource constraints, and strategic alignment opportunities.
- Federated Operating Model Enablement: Partner with business\-unit AI Catalyst leads to ensure consistent enterprise governance while enabling appropriate business\-unit autonomy.
- Executive Strategy \& Communications: Provide governance and portfolio insights for AI strategy, OKRs, executive updates, steering committees, and board\-level reporting.
- Team Building \& Capability Scaling: Build the foundational governance and portfolio function, then support the business case, operating model, and hiring roadmap for a dedicated team.
Qualifications:
- Education: Bachelor’s degree in Computer Science, Data Science, Information Systems, Business, or related field, or equivalent practical experience. Master’s degree preferred.
- Governance \& Portfolio Expertise: Experience building or operating governance, risk, data, technology, AI/ML, or portfolio management functions.
- AI Risk Framework Fluency: Familiarity with responsible AI practices and frameworks such as NIST AI RMF, ISO 42001, EU AI Act concepts, or similar models.
- AI/ML Lifecycle Understanding: Working knowledge of AI/ML use case frameworks, business case development, delivery concepts, model development, evaluation frameworks, monitoring, drift detection, human oversight, and production readiness.
- Portfolio Tooling \& Reporting: Experience with portfolio management tools and practices such as ServiceNow OneTrust, Wrike, or similar platforms preferred.
Leadership \& Experience
- Functional Build Experience: 5\+ years in technology, data, AI/ML, risk, governance, or portfolio\-management roles, with experience building or maturing enterprise capabilities.
- Matrixed Influence: Proven ability to drive alignment across business, technical, legal, compliance, privacy, security, and finance stakeholders without relying solely on direct authority.
- Strategic Execution: Ability to convert ambiguous enterprise needs into practical operating models, governance workflows, reporting rhythms, and measurable portfolio disciplines.
- Regulated Industry Knowledge: Experience in insurance, healthcare, financial services, or another regulated industry is preferred.
- Team Scaling: Experience developing operating models, role definitions, hiring plans, or team structures for new organizational capabilities preferred.
Executive Competencies
- Analytical Rigor: Ability to evaluate AI investments using business value, risk, feasibility, data readiness, operational complexity, and strategic alignment.
- Strategic Communication: Ability to synthesize complex governance, risk, portfolio, and technical information into clear executive narratives.
- Builder Mentality: Comfort operating in ambiguity, creating structure, and personally executing foundational work before a larger team is in place.
- Regulatory Judgment: Ability to translate legal, compliance, and responsible AI expectations into practical governance controls.
- Enterprise Partnership: Collaborative approach to working across federated business units while maintaining enterprise standards and accountability.
Benefits:
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 $159,000 \- $210,000 annually, 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\-Remote
\#LI\-FP1
Salary Context
This $159K-$210K range is above the median 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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($184K) sits 16% below the category median. Disclosed range: $159K to $210K.
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.
Enlyte AI Hiring
Enlyte has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $210K - $210K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.