Interested in this AI/ML Engineer role at AEG Vision?
Apply Now →Skills & Technologies
About This Role
AEG Vision is seeking a Director of Artificial Intelligence to lead the architecture, and hands-on implementation of AI solutions across the enterprise. This role is both strategic and deeply technical-ideal for a leader who can design AI architecture, build and deploy models, and guide teams, not just manage vendors or research.
The Director of AI will define how AI is used across automation, patient engagement, scheduling, revenue optimization, imaging, clinical operations, and internal systems, while ensuring solutions are scalable, secure, compliant, and practical for real-world healthcare environments.
Why Join AEG Vision
- Opportunity to define AI strategy from the ground up in a rapidly scaling healthcare organization
- Real-world impact on patient care, clinician experience, and operational efficiency
- Executive visibility and influence
- Balance of innovation, responsibility, and practical execution
Key Responsibilities
Enhance analytics through deep AI integration with the enterprise data warehouse
- Design and implement AI-enabled analytics solutions that sit natively on top of the enterprise data warehouse
- Partner with Data Engineering and BI teams to:
+ Embed machine learning, predictive analytics, and advanced forecasting directly into reporting and decision workflows
+ Enable self-service and augmented analytics for business users
Eliminate manual effort across complex back-office workflows through AI-enabled automation
- Identify high-friction, labor-intensive back-office processes suitable for AI-driven automation, including:
+ Revenue cycle and accounting workflows
+ Scheduling, capacity management, and exception handling
+ Data reconciliation, validation, and anomaly detection
+ Operational reporting and administrative processes
- Translate business problems into AI-driven solutions with measurable ROI.
- Continuously evaluate, pilot, and govern external AI platforms and vendors: Own the ongoing evaluation and governance of external AI tools, platforms, and vendors
Model Development & Implementation
- Personally contribute to:
+ Prototyping AI/ML models
+ Model selection and evaluation
+ Prompt engineering and orchestration for LLM-based systems
- Establish best practices for MLOps, model monitoring, versioning, and retraining.
Cross-Functional Collaboration
- Partner closely with:
+ Field Ops, Marketing, RCM, Accounting and Eyecare Operations
+ IT, Infrastructure, and Security teams
+ Product, Data, and Engineering teams
- Act as a translator between business and technical stakeholders.
- Guide responsible AI usage, governance, and compliance in healthcare settings.
Governance, Ethics & Compliance
- Ensure AI solutions adhere to:
+ HIPAA and healthcare data privacy requirements
+ Security and access control best practices
+ Ethical AI principles and explainability where required
- Define policies for data usage, model validation, and risk management.
Requirements:
Education
- Bachelor's degree in Computer Science, Engineering, Data Science, or related field required
- Master's degree or PhD preferred but not required with equivalent experience
Experience
- 5+ years of experience in software engineering, data science, or AI/ML roles
- Demonstrated experience building and deploying AI systems in production
- Experience in healthcare, health tech, SaaS, or highly regulated environments preferred
Technical Skills (Must Have)
- Strong programming background (Python required; SQL required)
- Hands-on experience with:
+ Machine learning frameworks (TensorFlow, PyTorch, scikit-learn)
+ Data engineering tools and pipelines
+ Cloud AI/ML services
- Experience designing AI architectures that integrate with enterprise systems
- Working knowledge of:
+ APIs and microservices
+ Data security and privacy
+ MLOps and model lifecycle management
Bonus Skills
- Experience with LLMs, generative AI, and retrieval-augmented generation (RAG)
- Experience with optimization, forecasting, or scheduling algorithms
- Familiarity with medical imaging, clinical data, or EHR integrations
- Experience evaluating and managing AI vendors vs in-house build decisions
Leadership & Personal Attributes
- Hands-on builder mindset with architectural depth
- Strong communicator able to explain AI concepts to non-technical audiences
- Pragmatic, ROI-focused approach to AI adoption
- Comfortable operating in a fast-growing, multi-site healthcare environment
- High integrity and respect for patient data and clinical workflows
Req Benefits: Benefits: 401(k) with Match, Medical/Dental/Life/STD/LTD, Vision Service Plan, Employee Vision Discount, Program HSA/FSA, PTO, Paid Holidays \*Benefits applicable to Full Time Employment only\*
Compensation: $170K to $180K per year
Salary Context
This $170K-$180K range is above the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At AEG Vision, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($175K) sits 14% above the category median. Disclosed range: $170K to $180K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
AEG Vision AI Hiring
AEG Vision has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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.