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About This Role
You Belong at Greenway
Bring your best and truest self. We celebrate what makes us different and what brings us all together. At Greenway Health, we are committed to an inclusive environment and a culture of belonging as we pursue our purpose of healthier communities, successful providers, and empowered patients. We are united in our goal to build the future of healthcare technology. Join us.
We are seeking a strategic and execution\-focused Director of AI Enablement to lead the adoption of AI\-driven workflows across the organization. This role is responsible for identifying high\-impact opportunities, designing scalable solutions, and embedding agentic AI capabilities into core business processes.As a key leader within the Technology organization, you will bridge business needs and technical capabilities by partnering with functional leaders to transform how work gets done through intelligent automation, AI agents, and modern workflow design.You will also build and lead a high\-impact team—including AI practitioners and interns—focused on delivering measurable business outcomes through AI enablement initiatives.
Essential Duties and Responsibilities
AI Strategy \& Business Transformation:
- Define and execute the organization’s AI Enablement strategy, aligned to business priorities.
- Partner with leaders across departments (e.g., operations, product, finance, customer support) to identify and prioritize AI use cases.
- Design and oversee the implementation of agentic workflows that automate tasks, augment decision\-making, and improve efficiency.
- Establish frameworks for evaluating, selecting, and deploying AI tools, platforms, and vendors.
- Build and manage a pipeline of AI initiatives, from ideation through production deployment.
- Lead, mentor, and develop a team focused on AI workflow design and implementation (including interns and early\-career talent).
- Drive adoption through training, documentation, and internal evangelism of AI capabilities.
- Define success metrics and track ROI of AI initiatives across the organization.
- Ensure responsible and secure use of AI, including governance, compliance, and risk management practices.
- Stay current on emerging trends in AI, agent\-based systems, and automation, and translate them into practical applications.
Important Notice Regarding Recruiting Practices
Greenway Health does not accept unsolicited agency or third\-party recruiter submissions unless the agency has been formally authorized in writing by Greenway Health. Any resumes or candidate information submitted without prior authorization will not be considered.
In addition, Greenway Health will never request personal financial information, payment of any kind, or sensitive data (such as Social Security numbers or bank details) during the application or interview process. If you are contacted by anyone claiming to represent Greenway Health and they request money, personal information, or anything unusual, please treat the communication as suspicious and report it immediately to Recruiting@greenwayhealth.com.
*Greenway Health is committed to maintaining a secure, transparent, and trustworthy hiring process.*
Education and Experience:
- 8\+ years of experience in technology, digital transformation, automation, or AI\-related roles.
- Proven experience leading cross\-functional initiatives and driving organizational change.
- Strong understanding of AI/ML concepts, particularly LLMs, automation, and workflow orchestration.
- Experience designing or implementing automation or AI\-enabled business processes.
- Ability to translate complex technical capabilities into clear business value
- Strong leadership, communication, and stakeholder management skills.
- Track record of delivering measurable business outcomes.
### Nice to Have
- Hands\-on experience with AI tools and frameworks (e.g., OpenAI APIs, LangChain, RPA tools, workflow automation platforms).
- Experience with agentic systems or multi\-step AI workflows.
- Background in consulting, product management, or enterprise transformation.
- Familiarity with data governance, security, and compliance considerations in AI.
### What You'll Build
- A scalable AI Enablement function that accelerates adoption across the organization.
- Repeatable frameworks for identifying, designing, and deploying AI workflows.
- A culture of innovation where teams leverage AI to work smarter and faster.
### Why This Role Matters
This role is central to how the organization evolves in the age of AI. You will directly shape how teams operate, unlocking productivity and enabling new ways of working through intelligent systems.
Work Environment/Physical Demands
- While at work, this position is primarily a sedentary job and requires that the associate can work in an environment where they will consistently be seated for the majority of the workday
- This role requires that one can sit and regularly type on a keyboard the majority of the workday
- This position requires the ability to observe a computer screen for long periods of time to observe their own and others’ work, as well as in\-coming and out\-going communications via the computer and/or mobile devices
- The role necessitates the ability to listen and speak clearly to customers and other associates
Here’s what we can offer you in exchange for your amazing work:
- Competitive pay
- Medical, dental and vision benefits
- Matching 401(k)
- Generous paid time\-off programs
- Education reimbursement
- Growth potential for your career
- Corporate discounts
At Greenway, we strive to imagine, empower, engage, and inspire. Join us!
To learn more about Greenway, take a video tour of our office, and meet our employees, visit us at www.GreenwayHealth.com/careers.
Disclaimer: This Job Summary indicates the general nature and level of work expected of the incumbent(s). It is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities required of the incumbent. Incumbent(s) may be asked to perform other duties as requested. Greenway Health, LLC is an Equal Opportunity Employer. We do not discriminate on the basis of race, religion, age, gender, national origin, sexual orientation, disability, or veteran status.
While this position is primarily remote, please note that if you reside within a 26\-mile radius of our corporate office, you will be required to work in a hybrid capacity. This means you will be expected to work on\-site at the corporate office for part of the week and remotely for the remainder. This hybrid arrangement is designed to foster team collaboration and engagement. Our corporate office is located at 4301 Boy Scout Blvd, Tampa, FL 33607\. Please consider your proximity to this location when applying.
If you are a resident of a state that requires pay transparency, please email us at recruiting@greenwayhealth.com to receive compensation and benefits information for this role. Be sure to include the Job ID in the subject line of your email.
\#LI\-REMOTE
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 Greenway 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. Director-level AI roles across all categories have a median of $272,150.
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.
Greenway Health AI Hiring
Greenway Health has 2 open AI roles right now. They're hiring across AI Software Engineer, 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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