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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
The Principal Network Engineer – AI Infrastructure plays a key role in building the high‑performance network infrastructure that powers the organization’s AI and GPU‑driven workloads. This position is responsible for designing and delivering scalable data center solutions that support large‑scale training and inference platforms. By leveraging modern architectures such as leaf‑spine fabrics, and aligning with leading vendor and industry reference designs, the role helps enable reliable, high‑throughput environments that directly support critical business initiatives.
Working closely with engineering, platform, and security partners, this role helps connect network, compute, and security capabilities into a cohesive, high‑performing ecosystem. In addition to hands‑on technical contribution, the position provides guidance on best practices, supports the development of other engineers, and helps shape the future direction of the organization’s AI infrastructure. Through continuous improvement, thoughtful design, and a focus on performance and resilience, this role contributes to a secure and scalable foundation that supports long‑term growth and innovation.
Role Responsibilities:
Collaboration \& Expertise
- Partner with compute, storage, platform, and security teams to design integrated AI infrastructure solutions.
- Serve as a senior technical authority aligning network designs with NVIDIA, Cisco, and industry reference architecture.
- Influence enterprise network and security strategy through collaboration with engineering leadership and stakeholders.
Analysis \& Configuration
- Design and implement high\-performance data center networks optimized for AI/GPU workloads, including leaf‑spine and EVPN/VXLAN fabrics.
- Integrate networking with GPU clusters and high\-performance storage systems supporting training and inference workloads.
- Optimize network performance (latency, throughput, congestion) for large\-scale distributed environments.
- Evaluate and deploy advanced networking technologies to improve scalability, reliability, and security.
Operational Support
- Support 24/7 infrastructure operations, including on\-call responsibilities across cloud, on\-prem, and colocation environments.
- Lead incident response and resolution for network\-related issues, driving root cause analysis and resilience improvements.
Mentorship and Training
- Mentor and develop engineers, promoting best practices in networking and security.
- Support knowledge sharing through training sessions and technical enablement.
Innovation and Research
- Evaluate and adopt emerging AI infrastructure and networking technologies (e.g., high\-speed interconnects, next gen switching).
- Contribute to research, innovation, and continuous improvement of network and security capabilities.
Strategic Planning
- Define and drive the data center network strategy supporting AI/ML platforms and business initiatives.
- Establish standards and reference architecture aligned with industry best practices.
- Guide long\-term roadmap decisions, balancing performance, scalability, security, and risk.
Required Qualifications
- 10\+ years of experience in network engineering, with at least 5\+ years in a leadership, architectural, or lead engineering role delivering enterprise or cloud network initiatives end\-to\-end.
- 5\+ years of experience designing and operating large\-scale data center networks, including Layer 2/3 architectures (leaf\-spine/Clos), EVPN/VXLAN overlays, and high\-speed networking (100/200/400Gb\+).
- 5\+ years of experience with enterprise routing, switching, and network platforms, including Cisco\-centric data center fabrics, protocols (BGP, OSPF, MPLS, STP), and hybrid connectivity (SD\-WAN, VPN, remote access).
- 5\+ years of experience implementing network security technologies, including Palo Alto Networks firewalls (required), NGFW, IDS/IPS, ZTNA, DLP, and micro\-segmentation, with understanding of application\-aware and zero trust architectures.
- 3\+ years of experience supporting AI/ML or GPU\-based environments, including NVIDIA reference architectures and performance\-optimized networking for distributed training workloads (e.g., traffic flow optimization, congestion management).
- 3\+ years of experience with application delivery and observability technologies, including F5 load balancing, network performance monitoring tools (e.g., NetFlow, Wireshark, SolarWinds), and traffic analysis for performance tuning.
Preferred Qualifications
- Experience designing and supporting AI factory / GPU cluster environments at scale (training and inference platforms).
- Familiarity with high\-performance compute networking enhancements (RDMA over Converged Ethernet – RoCE, PFC, ECN).
- Experience with Cisco Nexus, ACI, or equivalent data center switching platforms supporting AI workloads.
- Strong technical expertise with Networking and Software\-Defined Networking (SDN) principles.
- Strong technical expertise with developing and interpreting Network, Sequence, and Dataflow diagrams.
- Understanding of at least one compliance framework (HIPAA, HITRUST, PCI, NIST, CSA).
- Strong technical expertise in defining and implementing cyber resilience standards, policies, and programs for distributed cloud and network infrastructure, ensuring robust redundancy and system reliability.
- Experience in influencing industry standards and contributing to open\-source projects or security communities, highlighting a broader impact beyond the immediate organizations.
- Experience with network automation and Infrastructure as Code
- Background in high\-availability and disaster recovery design
- Certifications: CCIE/CCNP, JNCIE, AWS/Azure/GCP Networking, PCNSE/PAN or Security Specialty, CISSP
Education
- Bachelor’s degree or equivalent experience (High School Diploma and 4 years relevant experience)
Pay Range
The typical pay range for this role is:
$144,200\.00 \- $288,400\.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: 06/18/2026
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
Salary Context
This $144K-$288K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($216K) sits 19% above the category median. Disclosed range: $144K to $288K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CVS Health AI Hiring
CVS Health has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist. Positions span Albany, NY, US, Scottsdale, AZ, US, NY, US. Compensation range: $158K - $288K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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