CVS Health is actively hiring for 10 AI and machine learning positions across AI/ML Engineer (5), Data Scientist (3), and LLM Engineer (1) roles. Posted salary ranges span $144K - $288K, with 100% of listings disclosing compensation. The median posted ceiling sits at $203K. Positions are based in Hartford, CT, US, Richardson, TX, US, Woonsocket, RI, US. The most frequently requested skills across these postings are Python, Rag, Azure, Gcp, Prompt Engineering. Senior-level roles account for 60% of openings.
Skills & Technologies
Locations
Hartford, CT, US, Richardson, TX, US, Woonsocket, RI, US, MA, US, NY, US
Hiring by Role Category
Open Positions (10)
AI/LLM Engineer
AI Engineer
Senior Data Scientist
Staff Software Engineer (Agentic AI & Cloud Solutions)
Senior Data Scientist - Clinical AI
Manager, AI Service Operations Transformation PMO
Principal Data Scientist
Senior Manager - Digital Product (AI)
Director , Product Management - AI Claims Platform
What CVS Health's hiring tells you
10 open AI roles across 4 role types puts this company in the scaling phase: past the initial proof of concept, building out a real team. Expect more structure than a startup but less bureaucracy than a major. Good fit for engineers who want ownership without building from zero. Posted compensation range ($144K - $288K) suggests transparent and competitive pay practices.
The skill mix here leans toward Python in LLM Engineer roles. That is a clue about what CVS Health is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the CVS Health interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- What problem did the first AI hire solve, and how has scope grown since?
- Where does AI sit in the engineering org, and who owns the budget?
- What is the on-call expectation for AI systems? (If unclear, that means it has not happened yet.)
CVS Health AI and ML Hiring
CVS Health has 10 active AI and ML roles in our dataset. Open positions span LLM Engineer, AI/ML Engineer, Data Scientist, AI Software Engineer. Compensation ranges from $144K - $288K across disclosed roles. Roles are based in Hartford, CT, US, Richardson, TX, US, Woonsocket, RI, US, MA, US.
Salary Benchmarks
The market median for AI roles is $217,500. LLM Engineer roles pay a median of $203,940 across the market. AI/ML Engineer roles pay a median of $218,750 across the market. Data Scientist roles pay a median of $192,890 across the market. Top-quartile AI compensation starts at $272,100.
Skills CVS Health Looks For
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
AI Role Categories
LLM Engineer
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Market compensation for LLM Engineer roles: $203,940 median across 9 positions with disclosed pay.
AI/ML Engineer
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.
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.
Market compensation for AI/ML Engineer roles: $218,750 median across 3,817 positions with disclosed pay.
Data Scientist
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Market compensation for Data Scientist roles: $192,890 median across 463 positions with disclosed pay.
AI Software Engineer
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Market compensation for AI Software Engineer roles: $219,250 median across 424 positions with disclosed pay.
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.
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).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
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