Interested in this AI/ML Engineer role at CVS Health?
Apply Now →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
Ensures efficient and effective development, deployment, and support of Artificial Intelligence (AI) solutions by planning and designing AI-enabled experiences, collaborating with stakeholders, and staying updated with industry trends. Contributes to improved quality, faster delivery, enhanced productivity, and member engagement by developing and maintaining conversational AI and voice automation solutions, creating robust and reusable integrations, monitoring production performance, and integrating AI capabilities into existing technology environments.
Responsibilities
- Assists in the management of Artificial Intelligence (AI) initiatives by setting technical goals, providing guidance, and ensuring effective delivery and support in order to maintain and innovate new artificial intelligence solutions for the organization.
- Implements processes to identify areas where AI, including conversational AI and voice AI, can be leveraged to improve healthcare processes, enhance member and provider experiences, and optimize operations.
- Supports AI projects from conception to completion, ensuring adherence to timelines, budgets, quality standards, organizational priorities, and business objectives.
- Analyzes requirements, assesses feasibility, and proposes AI solutions, conversational experiences, prompts, and platform capabilities in order to address new initiative challenges effectively.
- Collaborates with AI engineers, architects, analysts, and business stakeholders to develop and implement conversational AI, generative AI, and voice AI solutions through configuration, integration, testing, and performance optimization in order to create accurate, reliable, and scalable AI experiences.
- Helps establish and implement robust integrations and data pipelines by addressing data privacy and security concerns and maintaining data governance practices in order to ensure the availability and quality of data required for AI projects.
- Implements processes for the evaluation, testing, monitoring, and continuous improvement of AI solutions to measure their performance, accuracy, effectiveness, user experience, and business impact.
- Collaborates with cross-functional teams through regular, timely communication and technical guidance to ensure delivery of exceptional services, adoption of best practices, and continuous improvement.
- Assists in the management of new AI deployments into the organization in order to ensure seamless integration with existing infrastructure, address technical challenges, monitor the performance of deployed AI systems, and provide ongoing production support.
- Configures, administers, and supports AI platforms, tools, and environments, including conversational AI technologies, to ensure operational stability, scalability, reliability, and effective release management.
- Monitors production AI solutions to identify performance trends, troubleshoot issues, resolve incidents, and maintain system health and availability.
- Develops and maintains technical documentation, implementation standards, operational procedures, runbooks, and support materials to enable sustainable delivery and support of AI solutions.
- Evaluates emerging AI technologies, platforms, and capabilities and provides recommendations to support innovation and continuous improvement.
- Supports organizational AI governance, compliance, security, and responsible AI practices to ensure solutions are deployed and operated in accordance with established standards.
Required Qualifications
- 5-7 years work experience
- Adept at problem solving and decision making skills
- Adept at collaboration and teamwork
- Adept at growth mindset (agility and developing yourself and others) skills
- Adept at execution and delivery (planning, delivering, and supporting) skills
- Experience developing, implementing, supporting, or maintaining enterprise technology solutions
- Experience integrating applications, systems, APIs, or platforms in a complex technology environment
- Experience supporting production applications and resolving operational issues
Preferred Qualifications
- Experience with conversational AI, virtual assistant, chatbot, voice automation, or related AI technologies preferred.
- Experience with AI platforms such as Kore.ai or similar enterprise AI technologies preferred.
- Experience with Generative AI, large language models (LLMs), prompt design, and AI orchestration preferred.
- Experience with speech, transcription, natural language processing, or voice technologies preferred.
- Experience with AI monitoring, observability, testing, or performance measurement preferred.
- Experience within healthcare, Medicare, member engagement, provider engagement, or regulated industries preferred.
- Experience with cloud-based technology platforms and AI solution deployments preferred.
Education
- Bachelor's degree preferred/specialized training/relevant professional qualification.
Anticipated Weekly Hours
40Time Type
Full timePay Range
The typical pay range for this role is:
$92,700.00 - $203,940.00 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.
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: 11/30/2026 Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
Salary Context
This $92K-$203K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($148K) sits 30% below the category median. Disclosed range: $92K to $203K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CVS Health AI Hiring
CVS Health has 9 open AI roles right now. They're hiring across Data Scientist, AI Product Manager, AI/ML Engineer, MLOps Engineer. Positions span New York, NY, US, Hartford, CT, FL, US. Compensation range: $173K - $334K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.