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About This Role
Last year our HCA Healthcare colleagues invested over 156,000 hours volunteering in our communities. As an engineer with HCA Healthcare you can be a part of an organization that is devoted to giving back!
##### Job Summary and Qualifications
We are enabling the organization to leverage AI technologies while maintaining strong security, privacy, and compliance standards. The Senior AI/Data Security Engineer partners closely with AI and data product teams to implement and operationalize security controls across AI platforms and data solutions.
This role focuses on hands\-on security engineering, vulnerability management, threat modeling, and secure pipeline integration. The position supports established AI security frameworks and collaborates with architects and senior security leaders to ensure AI and data products meet organizational security standards.
Major Responsibilities
Security Engineering \& Implementation
- Conduct security testing and vulnerability assessments aligned with enterprise data security standards.
- Perform threat modeling for AI and data solutions to identify and mitigate security risks.
- Integrate automated security scans and controls into CI/CD pipelines.
- Ensure security checks are incorporated into data and model deployment pipelines.
- Implement and support monitoring tools for data/MLOps environments to detect potential security incidents.
AI \& Data Security Support* Apply established AI security frameworks and standards to ML and LLM\-based solutions.
- Assist in operationalizing measurable AI security objectives and KPIs.
- Collaborate with Enterprise Security Architecture and AI Security Architects to implement roadmap initiatives.
- Stay current on emerging AI security threats, including adversarial ML and LLM risks.
Collaboration \& Delivery* Partner with developers and product teams to embed secure\-by\-design principles into AI and data products.
- Collaborate with Legal, Privacy, Risk, and Information Protection teams to support timely security reviews and approvals.
- Participate in third\-party security assessments and vulnerability testing engagements.
- Serve as a security advisor to development pods on operationalizing threat detection and vulnerability remediation.
Documentation, Monitoring \& Reporting* Document identified security issues and track remediation through resolution.
- Contribute to security reporting and dashboards that provide visibility into AI and data security posture.
- Maintain security documentation aligned with regulatory and enterprise requirements.
- Ensure compliance with applicable healthcare regulations (e.g., HIPAA, HITECH).
Training \& Continuous Improvement* Support security awareness initiatives for AI and data development teams.
- Recommend security improvements where gaps are identified and track implementation progress.
- Continuously refine controls and processes based on lessons learned and evolving threat landscapes.
- Performs other duties as assigned
- Practices and adheres to the “Code of Conduct” philosophy and “Mission and Value Statement.”
###### What qualifications you will need:
- Bachelors Degree in Information Systems preferred
- Bachelor's Degree required
- Five or more years of experience in a security, cloud or software engineering role
Licenses, Certifications and Training
- CISSP
- CISM
- CEH
- Cloud security certifications (e.g., Google Cloud, AWS, Azure)
Knowledge, Skills Abilities, Behaviors:
Required:
- Experience securing cloud environments (preferably Google Cloud).
- Strong understanding of encryption, IAM, data protection, and cloud\-native security controls.
- Experience integrating security controls into CI/CD pipelines.
- Proficiency in Python or other scripting languages used in Data/ML environments
- Knowledge of HIPAA, HITECH, and healthcare regulatory requirements.
- Familiarity with DevSecOps practices, CSPM tools, and security monitoring platforms.
- Strong analytical and problem\-solving skills.
- Ability to collaborate effectively with cross\-functional teams.
Preferred:* Familiarity with ML lifecycle, model training workflows, and LLM security considerations.
- Knowledge of adversarial ML risks and AI security research.
- Experience with data cataloging and classification tools.
Behavioral Competencies* Demonstrates integrity and commitment to organizational values.
- Communicates clearly with technical and non\-technical stakeholders.
- Makes informed decisions within defined governance structures.
- Drives execution through collaboration rather than formal authority.
- Adapts effectively to evolving security and AI landscapes.
##### Benefits
HCA Healthcare, offers a total rewards package that supports the health, life, career and retirement of our colleagues. The available plans and programs include:
- Comprehensive benefits for medical, prescription drug, dental, vision, behavioral health and telemedicine services
- Wellbeing support, including free counseling and referral services
- Time away from work programs for paid time off, paid family leave, long\- and short\-term disability coverage and leaves of absence
- Savings and retirement resources, including a 401(k) Plan with a 100% match on 3% to 9% of pay (based on years of service), Employee Stock Purchase Plan, flexible spending accounts, preferred banking partnerships, retirement readiness tools, rollover support and financial wellbeing counseling
- Education support through tuition assistance, student loan assistance, certification support, dependent scholarships and a partnership with Galen College of Nursing
- Additional benefits for fertility and family building, adoption assistance, life insurance, supplemental health protection plans, auto and home insurance, legal counseling, identity theft protection and consumer discounts
Learn more about Employee Benefits
*Note: Eligibility for benefits may vary by location.*
HCA Healthcare has been recognized as one of the World's Most Ethical Companies® by the Ethisphere Institute more than ten times. In recent years, HCA Healthcare spent an estimated $3\.7 billion in cost for the delivery of charitable care, uninsured discounts, and other uncompensated expenses.
"There is so much good to do in the world and so many different ways to do it."\- Dr. Thomas Frist, Sr.
HCA Healthcare Co\-Founder
Be a part of an organization that invests in you! We are reviewing applications for our eng opening. Qualified candidates will be contacted for interviews. Submit your application and *help us raise the bar in patient care!*
*We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.*
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 HCA Healthcare, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
HCA Healthcare AI Hiring
HCA Healthcare has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Nashville, TN, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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