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About This Role
We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time.
Position Summary
We are seeking a Staff Application Security Engineer – AI \& Agentic Systems to help secure the next generation of AI\-enabled applications, large language model integrations, and agentic systems. This role will partner closely with engineering, product, platform, and data teams to embed security into the design, development, and operation of both traditional and AI\-driven solutions.
As a senior technical contributor, you will help define secure design patterns, conduct threat modeling and risk assessments, guide engineering teams on secure development practices, and support the adoption of responsible AI security controls across the enterprise.
Key Responsibilities:
Secure Development, Standards \& Design
- Develop and maintain application and AI security standards, best practices, and guardrails.
- Promote secure\-by\-design principles across application and AI development lifecycles.
- Establish secure design patterns for AI agents, prompt management, tool integrations, memory handling, and autonomous workflows.
- Partner with engineering teams to identify and mitigate AI\-specific security risks such as prompt injection, model abuse, data leakage, and unauthorized agent actions.
AI \& Agentic Security Architecture
- Serve as a subject matter expert supporting the security of AI\-enabled applications and agentic systems.
- Review and provide guidance on architectures utilizing large language models, retrieval augmented generation pipelines, AI agents, and AI\-powered workflows.
- Define and recommend identity, authorization, data protection, observability, and governance controls for AI environments.
- Collaborate with AI platform, engineering, product, and data teams to ensure secure and responsible AI adoption.
Security Testing \& Risk Management
- Perform threat modeling, architecture reviews, and security assessments for applications and AI\-enabled systems.
- Conduct security analysis of AI workflows, model integrations, agent interactions, and data flows.
- Partner with engineering teams to prioritize and remediate security findings.
- Evaluate emerging AI security tools and technologies to improve organizational security posture.
Collaboration \& Technical Leadership
- Influence engineering teams to integrate security controls into development processes and product roadmaps.
- Partner with privacy, compliance, legal, and risk teams to align security controls with organizational requirements.
- Provide guidance on AI security considerations, emerging threats, and industry best practices.
- Participate in strategic initiatives supporting secure AI adoption across the enterprise.
Incident Response \& Continuous Improvement
- Support investigation and response efforts involving application and AI\-related security events.
- Assist in identifying root causes and implementing long\-term security improvements.
- Drive continuous improvement of security controls, processes, and engineering practices.
Mentorship \& Innovation
- Mentor security engineers and development teams on secure coding, threat modeling, and AI security best practices.
- Contribute to the evaluation of emerging AI security research, technologies, and industry standards.
- Help advance the organization's application and AI security strategy through innovation and technical leadership.
Required Qualifications
- 7\+ years of experience designing, building, or securing enterprise\-scale applications and platforms.
- 5\+ years of application security experience, including threat modeling, secure design, code review, and vulnerability management.
- 5\+ years of programming experience in one or more languages such as Python, Java, JavaScript, C\#, or Go.
- 3\+ years of experience developing or securing AI, machine learning, or generative AI solutions.
- 3\+ years of experience with public cloud platforms including AWS, Azure, or Google Cloud Platform.
Preferred Qualifications
- Experience securing modern application architectures, including APIs, containers, microservices, and serverless technologies.
- Strong understanding of secure software development lifecycle practices and application security testing methodologies.
- Hands\-on experience securing AI agents, retrieval augmented generation solutions, and large language model integrations.
- Experience conducting threat modeling and security assessments for AI\-enabled systems.
- Familiarity with responsible AI principles, AI governance, and emerging AI security frameworks.
- Experience integrating security controls into continuous integration and continuous delivery pipelines.
- Knowledge of common compliance frameworks such as PCI DSS, HIPAA, NIST, HITRUST, and CSA.
- Ability to influence technical decisions across multiple teams and organizations.
- Experience contributing to security research, open\-source projects, or industry communities.
Education
- Bachelor’s degree from an accredited college or university, or equivalent combination of education and relevant work experience (High School Diploma/GED plus 4 years of related experience)
Health100 is America's trusted front door to health and care. The Health100 platform integrates any participating health plan, PBM, pharmacy (retail and specialty), provider, digital health point solution provider, and employer, and addresses the top health care challenges for the consumer.
Pay Range
The typical pay range for this role is:
$106,605\.00 \- $284,280\.00
This pay range represents the base hourly rate or base annual full\-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short\-term incentive program in addition to the base pay range listed above. This position also includes an award target in the company’s equity award program.
Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong.
Great benefits for great people
We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families.
This full‑time position is eligible for a comprehensive benefits package designed to support the physical, emotional, and financial well‑being of colleagues and their families. The benefits for this position include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility.
Additional details about available benefits are provided during the application process and on Benefits Moments.
We anticipate the application window for this opening will close on: 08/24/2026
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
Salary Context
This $106K-$284K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CVS 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($195K) sits 9% below the category median. Disclosed range: $106K to $284K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CVS Health AI Hiring
CVS Health has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span New York, NY, US, TX, US, Richardson, TX, US. Compensation range: $185K - $334K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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