AI Engineer IV (Staff)

Overview:

The AI Engineer IV (Staff) is a senior technical leader within AssistRx Engineering and the company’s most experienced hands-on authority for artificial intelligence and machine learning. Reporting to the Vice President, Engineering, this role designs, builds, and deploys production-grade AI solutions across the specialty medication access ecosystem, including large language model applications, predictive models, intelligent document processing, and agentic automation that improve speed, accuracy, and scale in patient onboarding, benefits verification, prior authorization, document intake, and patient and provider engagement.

The AI Engineer IV (Staff) sets the technical direction for AI across multiple teams and products, owns the most complex and highest-risk AI solutions, and defines the architecture, evaluation standards, and responsible AI practices that other engineers build on. The role multiplies the impact of the engineering organization through technical leadership, mentorship, and reusable platforms, and partners closely with Software Engineering, Product, Architecture, Data, Security, Compliance, and Client Services to turn business problems into measurable outcomes. The AI Engineer IV (Staff) is accountable for solution quality, model performance, cost efficiency, and the safe and compliant use of AI with protected health information.

Responsibilities:

AI Solution Design & Development

• Lead the design, build, and deployment of complex, multi-system AI and machine learning solutions that advance AssistRx’s AI roadmap, including generative AI, predictive models, and intelligent document processing
• Architect applications on large language models using retrieval augmented generation, prompt engineering, structured outputs, function and tool calling, and agent-based orchestration
• Design agentic workflows with task decomposition, tool and function calling, memory and state management, routing, clear boundaries, human review checkpoints, and fallback paths for high-impact decisions such as benefits verification and prior authorization
• Lead the design of APIs, services, and integrations that embed AI capabilities into existing AssistRx platforms and client-facing workflows, resolving cross-team technical dependencies
• Translate ambiguous business and clinical workflow requirements into technical strategies, designs, and production releases, taking proven pilots to scale across products and clients
• Select the right approach for each problem, including prompting, retrieval augmentation, tool use, task-specific models, and conventional software or deterministic logic when a model is not the right answer
• Author and review design documentation for AI features covering intended behavior, context and retrieval design, prompt and agent structure, evaluation approach, and fallback behavior, setting the documentation standard for the organization

AI Foundations & Platform

• Own the AI reference architecture, reusable components, and engineering standards for AI development at AssistRx, including model access, prompt and version management, and retrieval services, and drive their adoption across engineering teams
• Evaluate commercial models, AI platforms, and vendor tools, and provide build-versus-buy recommendations to the Vice President, Engineering based on quality, cost, security, and time to value
• Shape the AI technical roadmap in partnership with Product and Architecture, helping prioritize work by business value, feasibility, and risk, and maintaining the AI use case inventory and technical backlog
• Identify platform gaps and technical debt in AI systems and lead the investments needed to close them

Model Development, Evaluation & Optimization

• Select, tune, and evaluate models against defined targets for accuracy, groundedness, hallucination rate, task completion, latency, and cost
• Define the organization’s evaluation frameworks, benchmark datasets, and automated test suites that measure output quality and detect regressions before each release
• Optimize retrieval pipelines, embeddings, chunking strategies, and inference performance to improve quality and reduce cost per transaction
• Lead error analysis and human-in-the-loop review cycles with operations and clinical subject matter experts, applying findings to improve prompts, models, and guardrails
• Design and run structured experiments and A/B tests across prompts, retrieval configurations, model selection,…