Interested in this AI/ML Engineer role at NMA Neuromonitoring Associates?
Apply Now →About This Role
Position Title: Director of AI Enablement
Department: Operations
Reports To: CEO & President/COO
FLSA: Exempt
Position Summary
The Director of AI Enablement provides strategic leadership for the organization's artificial intelligence initiatives, systems, and development processes. This role is responsible for overseeing AI-enabled solutions, managing the development lifecycle, and advancing the organization's long-term AI roadmap. Working closely with executive leadership and cross-functional stakeholders, the Director ensures AI initiatives deliver measurable business value while maintaining compliance with applicable healthcare regulations, data privacy requirements, and company policies. This position is accountable for AI governance, adoption, team leadership, and operational effectiveness across the organization.
Essential Duties & Responsibilities
- AI Strategy & Governance
+ Lead the transition, oversight, and continued enhancement of AI systems developed by external consultants and ensure continuity of operations following knowledge transfer. + Ensure AI systems and processes comply with organizational policies, HIPAA requirements, contractual requirements, cybersecurity, and other applicable regulatory requirements. + Establish and maintain governance practices related to AI development, data handling, access controls, auditability, and risk management. + Evaluate emerging technologies and industry trends to identify opportunities for innovation and operational improvement.
- Portfolio Management & Prioritization
+ Partner with executive leadership and operational stakeholders to identify, assess, and prioritize AI initiatives. + Evaluate proposed projects for business value, feasibility, resource requirements, and expected return on investment. + Maintain and communicate a prioritized portfolio of AI initiatives aligned with organizational objectives. + Provide recommendations regarding project selection, timing, and resource allocation.
- Solution Design & Delivery
+ Collaborate with operational teams to understand workflows, challenges, and opportunities for automation or augmentation. + Lead the design and implementation of AI-enabled solutions that improve efficiency, scalability, and business outcomes. + Oversee development processes, project planning, quality assurance, deployment activities, and ongoing system enhancements. + Ensure AI solutions are effectively integrated with existing technology platforms and operational processes.
- Team Leadership & Adoption
+ Evaluate, pilot, and implement AI capabilities including generative AI, intelligent assistants, workflow agents, predictive analytics, document intelligence, and other emerging technologies. + Provide leadership, direction, and performance oversight for team members and external partners as applicable. + Promote organizational adoption of AI-enabled tools through stakeholder engagement, communication, and training initiatives. + Foster a culture of innovation, accountability, and continuous improvement.
- Performance Measurement & Optimization
+ Establish success metrics and key performance indicators for AI initiatives. + Measure outcomes including administrative time eliminated, process efficiency, adoption, cost savings or avoidance, capacity created, error reduction, self-service, and other applicable measures. + Develop dashboards and regular executive reporting demonstrating the impact of NMA’s AI portfolio. + Identify opportunities for continued optimization and recommend that initiatives be adjusted or discontinued when expected value is not demonstrated.
Qualifications & Requirements
Education Licenses/Certifications
- Bachelor's degree in Computer Science, Information Technology, Data Science, Business, Engineering, Information Systems, or a related field.
Experience
- Seven or more years of experience in technology delivery, solution architecture, automation, systems implementation, software development, digital transformation, or related disciplines, including at least two years leading or supporting production AI, machine learning, AI agents, automated workflows, or Large Language Model (LLM) initiatives. Demonstrated success delivering business outcomes and production software using AI-assisted development tools may be considered in lieu of years of experience.
- Strong business acumen, emotional intelligence, and leadership capability, with the ability to guide organizations through AI-driven transformation, develop and defend business cases and ROI models, translate operational challenges into scalable technical solutions, and effectively partner with stakeholders across all levels of the organization.
- Proven ability to assume ownership of existing systems and processes, evaluate and govern AI-generated and delegated work, and deliver successful outcomes through cross-functional teams, contractors, vendors, offshore resources, and AI-enabled workflows while maintaining accountability for quality, accuracy, compliance, and results. Healthcare industry experience and familiarity with HIPAA compliance preferred.
Skills & Competencies
- Strong leadership, strategic planning, and organizational skills.
- Ability to evaluate business needs and translate them into scalable technology solutions.
- Knowledge of AI technologies, automation platforms, and emerging industry trends.
- Strong analytical, problem-solving, and decision-making abilities.
- Effective written and verbal communication skills with technical and non-technical audiences.
- Ability to manage multiple priorities and drive projects to completion.
- Experience developing business cases, measuring outcomes, and demonstrating return on investment.
- Ability to build collaborative relationships across all levels of the organization.
Working Conditions
- Work is generally conducted in a remote office environment.
- Frequent and prolonged periods of computer and office equipment usage.
- Ability to manage stress and maintain productivity under tight deadlines and changing priorities.
- Regular interaction with team members, clients, and other stakeholders.
Physical Demands
- Ability to maintain a stationary position for extended periods.
- Occasionally required to move short to moderate distances.
- Must be able to lift and/or move up to 50 pounds occasionally.
Disclaimer: The statements herein are intended to describe the general nature and level of work being performed by employees and are not to be construed as an exhaustive list of responsibilities, duties, and skills required of personnel so classified. Employees must be able to perform the essential functions of the position satisfactorily. Furthermore, the statements do not establish a contract for employment and are subject to change at the discretion of the Company with or without advance notice.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At NMA Neuromonitoring Associates, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
NMA Neuromonitoring Associates AI Hiring
NMA Neuromonitoring Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McKinney, TX, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.