Interested in this MLOps 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
CVS Health’s Analytics & Behavior Change (A&BC) is an organization working to solve some of the most challenging problems at the intersection of technology and healthcare. A&BC leverages advanced analytics, machine learning, modeling, and hypothesis-driven approaches to transform data into actionable, customer-centric insights driving growth, health outcome improvements, and access to health care across all our businesses in CVS Health. Our teams build next generation data and machine learning platforms and software products that help power CVS Health to make healthier happen for 100+ million customers.
The A&BC organization is looking to grow its Product team supporting clinical decision support intelligence. Join us as we embark on an exciting journey to drive a transformational shift in how CVS Health leverages technology and analytics to become the leader in consumer healthcare in the U.S.
As a Director within A&BC, you will be responsible for leading the creation, launch, and maintenance of an AI-powered, clinical data insights platform that powers both automation and workflow tools for Aetna Clinicians. This role requires a strong understanding of product management, clinical data, AI-powered insights acceleration, and cross-team collaboration.
The ideal candidate has familiarity with the following areas:
- Insight generation from unstructured clinical documentation using genAI, natural language processing and traditional ML, as well as experience building platform products.
- Reusable services designed to serve multiple downstream applications rather than a single use case.
- Working effectively with cross-functional teams, most importantly data scientists and machine learning engineers, to bring clinical data and AI products from inception to production.
This is a hands-on, individual-contributor role today. You'll help define how the platform team should be structured as scope and complexity grow, with the opportunity to build and lead that team over time.
Responsibilities:
- Develop a deep understanding of clinical and application-team needs through direct discovery, product data, and quantitative/qualitative analysis, to determine what should be built once and reused vs. left application-specific.
- Structure ambiguous problems, form and test hypotheses, and prioritize the platform investments with the highest clinical and business value.
- Define product strategy and own the platform roadmap, translating recurring needs from application/activation teams into reusable, scalable platform capabilities.
- Own multi-year roadmap and investment priorities, making evidence-based trade-offs across clinical value, team productivity, technical feasibility, reliability, and business outcomes.
- Lead the platform from concept through delivery in close partnership with Data Science and Engineering, making clear technical and product trade-offs (build vs. integrate vs. vendor).
- Partner with application teams to launch and scale platform capabilities, accelerate adoption, and identify barriers to reuse.
- Define platform health and success metrics: + Coverage, accuracy, reliability, adoption. + Using data and experimentation to inform roadmap and investment decisions.
- Influence senior stakeholders and align Data Science, Engineering, and cross-functional partners around platform strategy, priorities, and trade-offs.
Required Qualifications
- 8+ years of product management experience building enterprise software, platforms, or AI/ML and Generative AI products, including experience taking complex technical products from concept through launch, adoption, and iteration.
- 8+ years demonstrating exceptional analytical and product judgment, with ability to structure ambiguous problems and translate quantitative and qualitative evidence into strategy and measurable outcomes.
- 6+ years’ experience working with AI/ML and Generative AI products and application architectures, with technical fluency to partner deeply with Data Science and Engineering.
- 5+ years of experience influencing senior stakeholders and driving decisions across complex, cross-functional, matrixed organizations.
- 5+ years of experience demonstrating executive communication and storytelling skills, with the ability to translate fluidly between technical, clinical, and business audiences.
Preferred Qualifications
- Domain expertise in healthcare or health insurance, including familiarity working with unstructured clinical documentation.
- Experience building platform products and reusable services designed to serve multiple downstream applications rather than a single use case.
- Experience with healthcare data interoperability standards and multi-channel data intake (e.g., fax/EDI, EMR feeds).
- Experience evaluating emerging AI/ML technologies and external vendor solutions.
- Experience defining and measuring platform adoption and business outcomes.
Education
- Bachelor's degree required, preferably in a quantitative, analytical, or engineering field.
Pay Range
The typical pay range for this role is:
$100,000.00 - $231,540.00
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: 10/26/2026 Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
Salary Context
This $100K-$231K range is above the median for MLOps Engineer roles in our dataset (median: $157K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,653 AI roles we're tracking, MLOps Engineer positions make up 2% of the market. At CVS Health, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills in Demand for This Role
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 151 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($165K) sits 19% below the category median. Disclosed range: $100K to $231K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
CVS Health AI Hiring
CVS Health has 10 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, MLOps Engineer, AI Software Engineer. Positions span UT, US, Tallahassee, FL, US, TX, US. Compensation range: $231K - $334K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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