Staff Software Architect

CVS Health•Hartford, CT

About The Position

CVS Health is seeking a Staff Software Architect to serve as a senior individual-contributor technical leader on the Clinical Insights Engine (CIE) team. The role defines the platform’s long-term software and systems architecture vision, shapes engineering strategy across multiple squads and product verticals, and leads designs for mission-critical systems supporting clinical AI models, ML inference pipelines, enterprise reporting, and large-scale healthcare analytics across GCP and Azure. The architect provides technical direction and governance for scalable, fault-tolerant, cost-optimized platforms and production ML systems. This includes architecture for LLM-integrated systems, clinical NLP, document processing, real-time clinical data services, and MLOps capabilities. The role establishes engineering standards and reference architectures, aligns technical decisions with clinical and business outcomes, and represents CIE in enterprise architecture and data governance forums. The position also addresses architectural risk, technical debt, and data quality; mentors senior engineers and architects; and ensures systems comply with HIPAA, enterprise security standards, and clinical data governance requirements. A bachelor’s degree in a relevant field is required. A master’s degree or equivalent advanced technical experience is preferred. The role offers a base pay range of $106,605 to $260,590, plus eligibility for a CVS Health bonus, commission, or short-term incentive program and an equity award target. This full-time position is eligible for benefits including medical, dental, vision, paid time off, retirement savings options, wellness programs, and other resources, subject to eligibility.

Requirements

At least 8 years of experience in software or data architecture, platform engineering, or a closely related field.
At least 5 years of experience designing enterprise-scale cloud systems in GCP and/or Azure in a production healthcare or regulated-industry environment.
Deep expertise in cloud-native services relevant to the CIE stack, such as BigQuery, Dataflow, Vertex AI, Cloud Run, GKE, Pub/Sub, Cloud Storage, or Azure equivalents.
Demonstrated experience architecting clinical AI/ML platforms, including feature stores, model serving infrastructure, MLOps tooling, and LLM integration patterns applied to healthcare data.
Strong command of distributed systems design, including event streaming, asynchronous processing, API gateway patterns, service mesh, and fault-tolerant pipeline architecture.
Experience defining and executing multi-year platform roadmaps across large, multi-squad engineering programs.
Proven ability to influence and align senior technical and business stakeholders across portfolio and product boundaries without direct authority.
Deep familiarity with clinical and healthcare data domains, including clinical notes, EHR data, claims, pharmacy, member, and provider data.
Strong understanding of HIPAA, PHI handling requirements, clinical data privacy regulations, and enterprise security frameworks.
Experience working in SAFe Agile, program increment planning, and large-scale Agile delivery.
Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field.

Nice To Haves

Google Cloud Professional Architect, GCP Data Engineer, or Azure Solutions Architect certification.
Hands-on experience with LLM-based clinical document processing, RAG architectures, OCR pipelines, or clinical NLP applicable to patient fact extraction and potential conditions workflows.
Experience designing and operating production ML platforms at scale using Vertex AI Pipelines, Kubeflow, MLflow, or equivalent.
Familiarity with data mesh principles, domain-oriented data ownership, and federated governance.
Background in real-time ML serving, A/B testing infrastructure, and model monitoring and drift detection in clinical AI contexts.
Prior experience in health plan, healthcare delivery, or clinical operations environments; familiarity with clinical workflows and value-based care programs is a plus.
Published thought leadership, conference presentations, or patents in AI/ML systems, clinical NLP, or healthcare data architecture.
Master’s degree in Computer Science, Machine Learning, or equivalent advanced technical experience.

Responsibilities

Drive the end-to-end software architecture vision and roadmap for the Clinical Insights Engine platform across GCP and Azure.
Serve as technical authority for system design and architecture decisions, providing guidance and governance across engineering squads and product teams.
Lead the design of scalable, fault-tolerant, and cost-optimized platforms for clinical AI/ML workloads, including LLM inference pipelines, large-scale document processing, and real-time clinical data services.
Define and govern ML architecture patterns, including feature stores, model registries, model serving infrastructure, experiment tracking, and MLOps pipelines.
Lead architecture for LLM-integrated systems, including prompt engineering frameworks, RAG pipelines, embedding stores, and clinical NLP processing.
Define CIE-wide engineering standards, reference architectures, and platform patterns that promote consistency, reuse, and velocity.
Define enterprise software standards, architecture patterns, and reference architectures adopted across HCD and the broader CVS Health engineering community.
Partner with CIE portfolio leads, product managers, and clinical stakeholders to align technical architecture with clinical program outcomes and business strategy.
Represent CIE in enterprise architecture forums, HCD data governance councils, and cross-functional technical leadership groups.
Champion engineering practices across CIE squads, including event-driven design, API-first development, data quality at source, and self-service platform patterns.
Identify and proactively address architectural risk, technical debt, and data quality issues across CIE's clinical data platform.
Mentor senior engineers and architects across CIE squads and foster technical rigor, curiosity, and innovation.
Ensure CIE systems meet HIPAA compliance, enterprise security standards, and clinical data governance requirements.