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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.
We are seeking an experienced AI / LLM Engineer to design, develop, and operationalize advanced language model–powered applications for enterprise use cases. This role will focus on building text\-based reasoning systems, Retrieval\-Augmented Generation (RAG) pipelines, and scalable prompt engineering frameworks that enhance decision\-making, automation, and knowledge discovery across the organization.
As a key member of a cross\-functional team, you will collaborate with business stakeholders to translate business requirements into reliable, explainable, and production\-ready AI solutions. You will play a critical role in shaping enterprise AI capabilities by ensuring solutions are secure, responsible, and optimized for real\-world performance.
- Design and implement LLM\-powered applications that support complex, text\-based reasoning and decision workflows.
- Develop and refine chain\-of\-thought\-style reasoning approaches and structured prompt patterns to improve model accuracy and interpretability.
- Architect and build Retrieval\-Augmented Generation (RAG) systems leveraging embeddings, vector search, and hybrid retrieval strategies.
- Create, evaluate, and optimize prompt engineering frameworks, including reusable templates, prompt libraries, and testing methodologies.
- Implement monitoring, logging, and feedback loops for continuous improvement of AI systems.
- Ensure compliance with security, governance, and Responsible AI principles.
- Partner with product and analytics teams to rapidly prototype and iterate AI\-driven features.
Tools \& Technologies
- Programming: Python (primary), SQL
- LLM Platforms: OpenAI, Anthropic, Google Vertex AI
- Frameworks: LangChain, LlamaIndex, Semantic Kernel
- Vector Databases: Pinecone, Weaviate, FAISS, MongoDB Atlas Vector Search
- Data Processing: Spark, Pandas
- APIs \& Services: FastAPI, Flask, REST/gRPC
- Cloud Platforms: AWS, Azure, Google Cloud Platform (GCP)
- DevOps \& MLOps: Docker, Kubernetes, CI/CD tools
- Monitoring \& Evaluation: Prompt evaluation tools, logging frameworks, observability platforms
Essential Qualifications and Functions:
- 5\+ years expereince
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
- Strong programming experience in Python and familiarity with software engineering best practices.
- Hands\-on experience building production\-grade AI/ML systems, not just prototypes.
- Experience working with Large Language Models (LLMs) and APIs.
- Solid understanding of:
+ Natural Language Processing (NLP) fundamentals
+ Machine learning concepts (training, evaluation, overfitting, bias)
- Practical experience with:
+ Retrieval\-Augmented Generation (RAG) systems
+ Prompt engineering and prompt optimization
+ Embeddings and vector search
- Experience designing and implementing APIs, microservices, or distributed systems.
- Familiarity with model evaluation techniques and performance metrics.
- Strong debugging and problem\-solving skills in complex systems.
Preferred Qualifications:
- Experience with LLM platforms such as OpenAI, Anthropic, Google Vertex AI, or similar.
- Familiarity with orchestration frameworks like LangChain, LlamaIndex, Semantic Kernel, or equivalent.
- Experience with vector databases (e.g., Pinecone, Weaviate, FAISS, MongoDB Atlas Vector Search).
- Knowledge of MLOps practices, including CI/CD pipelines for AI systems.
- Experience deploying solutions in cloud environments (e.g., AWS, Azure, GCP).
- Exposure to agent\-based architectures or multi\-step AI workflows.
- Experience in financial services enterprise environments (or similar data\-intensive industries).
- Experience with evaluation frameworks and benchmarking for LLMs.
This role does not support sponsorship at this time
Anticipated Weekly Hours
40Time Type
Full timePay Range
The typical pay range for this role is:
$101,970\.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: 08/31/2026
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
Role Details
About This Role
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.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 3,708 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At CVS Health, this role fits into their broader AI and engineering organization.
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 the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
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.
Skills Required
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.
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.
Compensation Benchmarks
LLM Engineer roles pay a median of $203,940 based on 9 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($152K) sits 25% below the category median. Disclosed range: $101K to $203K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CVS Health AI Hiring
CVS Health has 10 open AI roles right now. They're hiring across LLM Engineer, AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span Hartford, CT, US, Richardson, TX, US, Woonsocket, RI, US. Compensation range: $144K - $288K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
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.
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.
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.
Frequently Asked Questions
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