Staff Full-Stack AI Engineer

The Staff Full-Stack AI Engineer (Level IV)is a senior technical leader within AssistRx Engineering. This is a software engineering role with an AI focus, not a narrowly specialized machine learning role. The engineer designs, builds, and deploys end-to-end production solutions across the specialty medication access ecosystem, including patient onboarding, benefits verification, prior authorization, document intake, and patient and provider engagement.

Depending on the business problem, the solution may include a modern web application, REST APIs and backend services, integrations, data workflows, cloud infrastructure, generative AI, predictive models, intelligent document processing, agentic automation, or a combination of these. The Staff Engineer is expected to select the simplest effective approach and to use conventional software or deterministic logic when AI is not the right answer.

The role partners closely with Software Engineering, Product, Architecture, Data, Security, Compliance, and Client Services. It owns complex cross-system solutions, establishes reusable engineering patterns, mentors other engineers, and is accountable for solution quality, reliability, security, maintainability, model performance, cost efficiency, and the safe and compliant use of protected health information.

Responsibilities

Full-Stack Solution Design And Delivery

• Lead the design, development, testing, deployment, observability, and production support of complete applications spanning user interfaces, APIs, backend services, integrations, data access, cloud services, and AI-enabled components.
• Build both GenAI functionality and conventional application functionality based on the needs of the initiative; do not limit ownership to the AI service layer.
• Translate ambiguous business and healthcare workflow requirements into clear technical strategies, designs, incremental releases, and measurable production outcomes.
• Design secure, maintainable REST APIs, event-driven services, system integrations, and responsive web experiences that integrate naturally with existing AssistRx platforms.
• Resolve cross-team dependencies and take proven pilots through production hardening and scale across products and clients.
• Set standards for CI/CD, source control, containerization, and infrastructure-as-code practices for AI workloads
• Serve as the escalation point for complex production issues and client-reported defects in AI-enabled features, leading root cause analysis and continuous improvement of deployed AI services

Architecture And Technology Selection

• Evaluate architectural alternatives and select languages, frameworks, data stores, integration patterns, and cloud services based on business fit, scalability, security, maintainability, cost, and delivery speed.
• Translate ambiguous business and clinical workflow requirements into technical strategies, designs, and production releases, taking proven pilots to scale across products and clients
• Work productively across multiple programming languages and architectural styles rather than defaulting to a single preferred stack.
• Author and review design documentation covering system boundaries, interfaces, data flows, security controls, failure modes, observability, evaluation, and fallback behavior.
• Identify technical debt and platform gaps, then lead pragmatic investments that improve delivery across multiple teams.
• Create reusable components, reference implementations, and engineering standards that accelerate both AI-enabled and conventional application development.

Applied AI Engineering

• Design and implement production LLM applications using retrieval-augmented generation, structured outputs, prompt design, function and tool calling, and agent-based orchestration.
• Develop interactive AI products such as chat, conversational interfaces, voice experiences, assistants, and agentic workflows that are integrated into complete user journeys.
• Design clear system boundaries, memory and state management, routing, human review checkpoints, fallback paths, and deterministic controls for high-impact workflows.
• Select, tune, and evaluate models against defined targets for accuracy, groundedness, hallucination rate, task completion, latency, and cost
• 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
• Establish practical evaluation, regression testing, monitoring, cost management, content safety, and responsible AI practices.
• Evaluate commercial models, AI platforms, and vendor tools and make evidence-based build-versus-buy recommendations.
• Build solutions that satisfy healthcare privacy, data security, and regulatory requirements, including HIPAA and HITRUST
• Partner with Security and Compliance to address AI-specific risks such as prompt injection, data leakage, and insecure output handling, and to confirm vendor agreements (including Business Associate Agreements) are in place before PHI is processed
• Define guardrails, content filtering, human oversight, and audit trails appropriate to the risk profile of each use case
• Document model design, data sources, intended use, and known limitations to support internal AI governance, audits, and client due diligence

Technical Leadership & Collaboration

• Provide hands-on technical leadership for complex initiatives and raise the engineering bar through code, architecture reviews, troubleshooting, and production readiness reviews.
• Mentor engineers across frontend, backend, cloud, integration, and AI development; share patterns that make teams more effective.
• Partner with Product Management and business stakeholders to prioritize work by business value, feasibility, risk, and total cost of ownership.
• Communicate technical decisions and trade-offs clearly to engineering, product, security, compliance, and business stakeholders.
• Participate in day-to-day agile team activities, including sprint planning, daily standups, sprint reviews, and retrospectives
• Apply and help improve AI-assisted and agentic development practices within the engineering organization, including specification-driven workflows and agentic coding tools, and share what works with engineering peers
• Conduct regular self-guided study to stay current in a fast-moving AI landscape, and bring relevant new techniques and tooling to the team’s attention

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
• 7+ years of professional software engineering experience building and operating production systems, with meaningful experience delivering AI or machine learning capabilities in production.
• Strong full-stack engineering experience, including modern web application development, REST APIs, backend services, data access, integrations, automated testing, and production operations.
• Production experience with at least two general-purpose application development languages, such as C#, Java, Python, TypeScript/JavaScript, Go, or comparable languages.
• Hands-on experience with a modern frontend framework such as React, Angular, Vue, or a comparable framework.
• Strong experience with cloud architecture, distributed systems, containers, CI/CD, infrastructure as code, observability, application security, and performance troubleshooting.
• Hands-on experience delivering LLM or GenAI capabilities within end-user applications, including RAG, tool/function calling, evaluation, guardrails, and monitoring.
• Demonstrated ability to learn unfamiliar technologies, compare architectural options, and choose the right implementation approach for the problem.
• Strong written and verbal communication, technical judgment, and experience mentoring engineers and leading cross-team technical work.

Preferred Qualifications

• Master’s degree in a relevant technical field.
• Experience in healthcare technology, specialty medication access, protected health information, or another highly regulated environment.
• Experience with conversational AI, voice applications, intelligent document processing, predictive models, or agentic workflows.
• Experience creating reference architectures or reusable platforms adopted by multiple engineering teams.
• Experience evaluating model quality, cost, latency, security, privacy, and operational risk in production AI systems.
• Experience with Docker, Kubernetes, or infrastructure-as-code
• Experience with agent and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Strands, or Model Context Protocol
• Experience with machine learning operations tooling, model registries, feature stores, and LLM observability or evaluation platforms
• Experience with model fine-tuning or smaller task-specific models for classification and extraction
• Familiarity with AI security guidance such as the OWASP Top 10 for LLM Applications
• Familiarity with HIPAA, HITRUST, or responsible AI frameworks such as the NIST AI Risk Management Framework
• Prior experience as an early or lead AI engineer helping an organization adopt AI

Competencies

• Technical Depth
• Analytical Rigor & Problem Solving
• Experimentation & Measurable Outcomes
• Accuracy & Quality
• Sound Judgment & Risk Awareness
• Ownership & Accountability
• Pragmatism & Bias for Delivery
• Communication
• Collaboration & Influence
• Technical Leadership & Mentorship
• Curiosity & Learning Agility
• Adaptability
• Initiative & Innovation
• Ethics & Integrity

PHYSICAL DEMANDS AND WORK ENVIRONMENT:

• Occasionally required to stand.
• Occasionally required to walk.
• Frequently required to sit.
• Continually required to utilize hand and finger dexterity.
• Continually required to talk or hear.
• Specific vision abilities required by this job include close vision and the ability to adjust focus.