Staff Machine Learning Engineer – Generative AI, Full-Stack Applications

CVS Health

Staff Machine Learning Engineer at CVS Health designing and prototyping AI-powered solutions. Collaborating with stakeholders and building production-grade services in enterprise AI/ML initiatives.

Posted 7/2/2026full-timeRemote • Illinois • 🇺🇸 United StatesLead💰 $106,605 - $284,280 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in software engineering for Data and AI/ML initiatives, with a strong focus on building production-grade services and implementing safety and compliance controls. Proficient in full-stack engineering, LLM application patterns, and optimizing solutions for high-scale environments.

Highest-signal resume keywords

GenAI DeliveryFull-Stack EngineeringLLM Application PatternsPython ProgrammingCI/CD Automation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringMachine LearningArtificial IntelligenceProduction ServicesAPIsWeb Application DevelopmentRAG PipelinesEvaluation HarnessesSecurity ControlsObservability

Soft Skills

Clear CommunicationInfluencing Across TeamsTranslating Business Needs

Tools & Technologies

KubernetesContainerizationInfrastructure-as-CodeML LibrariesServing Frameworks

Industry Keywords

High-Scale EnvironmentsHigh-AvailabilitySafety ControlsCompliance RequirementsPrompt Management

Tech Stack

Tools & technologies

KubernetesPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

7+ years of software engineering supporting Data or AI/ML initiatives, including building and operating production services.
3+ years applying ML/AI in production; demonstrated hands-on GenAI delivery (LLMs, RAG, evaluation, and safety controls)
3+ years of experience delivering solutions in high-scale, high-availability environments with strong security and compliance requirements.
Strong full-stack engineering skills (backend services, APIs, and modern web application development) with a focus on reliability and security.
Hands-on expertise with LLM application patterns: RAG, tool/function calling, prompt management, evaluation, and guardrails.
Experience with Python and at least one additional backend language; familiarity with common ML libraries and serving frameworks.
Working knowledge of containerization and Kubernetes, CI/CD, infrastructure-as-code concepts, and production observability.
Ability to communicate clearly, influence across teams, and translate business needs into implementable technical plans.

Benefits

Comp & perks

medical, dental, and vision coverage
paid time off
retirement savings options
wellness programs
comprehensive benefits package designed to support physical, emotional, and financial well-being