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. At CVS Health, our purpose is to deliver better health outcomes by meeting consumers where they are—through local care, digital experiences, and a nationwide team committed to quality, safety, and affordability. Our Solutions Engineering and Infrastructure organization is building an enterprise AI/ML capability that delivers reliable, responsible, and secure AI-powered platforms and solutions at Fortune 5 scale, and this role is foundational to help us develop that capability. This is an experienced individual-contributor role focused on evaluating, documenting, building and scaling GenAI and AI-powered capabilities, contributing to solution design, implementation, and production support in partnership with business partners and senior technical leaders. Key Responsibilities: Solution Development & Delivery - Implement AI-powered services and application features that integrate enterprise data and systems with LLMs and ML models. - Contribute to solution design by proposing approaches, identifying dependencies, and documenting tradeoffs and implementation plans. - Deliver features using strong engineering practices: test automation, code review, CI/CD, and operational readiness. GenAI Engineering Practices - Build and tune RAG pipelines (retrieval, ranking, grounding) and implement prompt patterns that improve reliability and user experience. - Contribute to evaluation suites and regression testing to ensure quality and safety over time. - Implement guardrails and security controls (input/output validation, policy filtering, and data protection patterns) under guidance from senior engineers. Reliability, Observability & Support - Instrument services with metrics, traces, logs, and dashboards; participate in incident response and post-incident improvements. - Support performance and cost optimization efforts (latency, throughput, caching, and model selection). - Follow and reinforce platform standards for deployment, versioning, monitoring, and documentation. Collaboration & Continuous Improvement - Work closely with product, design, security, and platform teams to refine requirements and deliver customer-focused outcomes. - Mentor engineers at earlier career stages and share practical learnings with the broader AI/ML community of practice. - Stay current on GenAI and ML platform trends; propose incremental improvements that raise quality and accelerate delivery. Required Qualifications - 5+ years of software engineering experience delivering production systems, including modern APIs and application development. - 3+ years of experience applying ML/AI concepts in real systems; GenAI exposure (LLMs, RAG, evaluation) strongly preferred. - 3+ years of experience delivering solutions in high-scale, high-availability environments with strong security and compliance requirements. Preferred Qualifications - Proficiency in backend engineering (APIs, services) and comfort contributing across the stack when needed. - Working knowledge of LLM application development (prompting, RAG, tool calling) and evaluation practices. - Experience with CI/CD, testing, containerization, and basic Kubernetes concepts. - Strong debugging and operational skills; ability to improve reliability and performance based on telemetry and root cause analysis. - Effective collaboration and communication skills across technical and non-technical stakeholders. Education - Bachelor’s degree or equivalent experience (High School Diploma and 4 years relevant experience) Anticipated Weekly Hours 40 Time Type Full time Pay Range The typical pay range for this role is: $83,430.00 - $222,480.00 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 our comprehensive and competitive mix of pay and benefits – investing in the physical, emotional and financial wellness of our colleagues and their families to help them be the healthiest they can be. In addition to our competitive wages, our great benefits include: - Affordable medical plan options, a 401(k) plan (including matching company contributions), and an employee stock purchase plan. - No-cost programs for all colleagues including wellness screenings, tobacco cessation and weight management programs, confidential counseling and financial coaching. - Benefit solutions that address the different needs and preferences of our colleagues including paid time off, flexible work schedules, family leave, dependent care resources, colleague assistance programs, tuition assistance, retiree medical access and many other benefits depending on eligibility. For more information, visit https://jobs.cvshealth.com/us/en/benefits We anticipate the application window for this opening will close on: 03/26/2026 Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.

• Design and train modern computer vision models (CNNs, Vision Transformers, foundation models) to solve novel perception tasks • Build and maintain scalable training pipelines using PyTorch and HPC infrastructure (e.g., Slurm, distributed training) • Develop data curation and active learning workflows • Optimize models for deployment (ONNX, TensorRT, containerization) • Test and validate models in both cloud and edge environments • Build reproducible experimentation workflows (version control, experiment tracking, configuration management) • Drive experimental cycles: define hypotheses, implement techniques from literature, evaluate results, and present recommendations • Translate research ideas into production-ready implementations • Design, implement, and test ML-related components and supporting software • Perform statistical analysis and fine-tune model performance based on production and field feedback

• Design and build enterprise-grade AI/ML systems end-to-end — from data pipeline through model development, deployment, and production monitoring — given functional and non-functional requirements. • Develop an experimentation roadmap. • Set up a reproducible experimentation environment and maintain experimentation pipelines. • Monitor and maintain ML models in production to ensure optimal performance. • Develop robust, production-quality Python code and reusable software modules that power data processing workflows, ML pipelines, and AI-driven applications — going well beyond notebook-style scripting. • Leverage cloud-native data and ML services (AWS stack preferred: SageMaker, EMR, S3, Lambda, ECR). • Conduct technical discovery workshops with enterprise clients, contributing to solution architecture and proposal development with a full-stack technical perspective. • Lead technical delivery for major client initiatives. • Drive adoption of MLOps best practices and technical standards. • Mentor junior engineers and shape the team's technical direction. • Evaluate and champion new technologies and frameworks.

• Partner with stakeholders to identify, evaluate, document, and shape GenAI use cases (copilots, automation, decision support, and insight generation) with clear success metrics. • Design solution architectures that integrate LLMs with enterprise systems, data sources, and tool/function calling while meeting latency and reliability expectations. • Develop prototypes rapidly and validate them through evaluation, red-teaming, and user feedback; document tradeoffs and recommendations. • Build production-grade services and full-stack experiences (APIs, UIs, workflows) with secure authentication/authorization, audit logging, and scalable deployment patterns. • Implement safety, privacy, and compliance controls (e.g., PHI/PII protection, prompt injection defenses, data residency constraints, and policy-based filtering). • Instrument solutions end-to-end with metrics, traces, logs, and model/app observability; contribute to SLOs, error budgets, and operational runbooks. • Build and maintain evaluation harnesses for LLM quality, safety, and business outcomes (offline tests, golden sets, regression suites, and online experiments). • Implement RAG pipelines (chunking, embedding, vector search, reranking) and optimize for accuracy, cost, and latency. • Collaborate with platform teams on deployment, monitoring, drift/quality detection, and incident response for model-backed services. • Contribute reusable libraries and patterns for prompt management, retrieval, tool calling, and policy enforcement. • Participate in design reviews and code reviews; mentor senior and mid-level engineers on GenAI engineering practices. • Continuously improve developer experience through templates, CI/CD automation, and documentation that accelerates safe adoption.