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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - Generative AI & Full-Stack Applications - **Company:** CVS Health - **Location:** Boise, ID, United States - **Experience:** Expert - **Salary:** $106,605.0 - $284,280.0 - **Contract:** Permanent contract - **Skills:** Adobe InDesign, Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Code Review, Continuous Integration, Decision Support Systems, Web Development, Python (Programming Language), Machine Learning, Rapid Prototyping Process, Search Technologies, Software Engineering, Enterprise Software Applications, Large Language Models, Generative AI, Backend, Containerization, Scikit Learn, Kubernetes - **Published:** July 2, 2026 - **Apply:** https://dejobs.org/x/x/250DEF8B09AA4C59A2F05723EE090175/job/ ## About the Role * 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., * Bachelor's degree or equivalent experience (High School Diploma and 4 years relevant experience) ## Description AI Solution Design & Prototyping * 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. Production Engineering & Enterprise Readiness * 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. Model Enablement & Evaluation * 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. Reusable Components & Engineering Excellence * 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. ## Related Videos - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Resilient by Design: Building Robust Architectures in High-Stakes Financial Systems](https://www.wearedevelopers.com/videos/2106-resilient-by-design-building-robust-architectures-in-high-stakes-financial-systems) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)