Principal Full-Stack Ai Engineer

OverviewThe Principal Full-Stack AI Engineer (Level V) is the most senior individual-contributor technical authority for AI-enabled software engineering within AssistRx Engineering. This is an enterprise software engineering role with deep AI responsibility, not a narrowly specialized machine learning role. The Principal Engineer defines technical strategy while remaining hands-on in the design and delivery of the company’s most complex systems.
The role spans conventional and AI-enabled architectures. Depending on the business problem, the Principal Engineer may lead a modern web application, REST and event-driven services, enterprise integrations, data workflows, cloud platforms, generative AI, predictive models, intelligent document processing, agentic automation, or a combination of these. The Principal Engineer is expected to choose technologies and languages based on the problem, and to recognize when conventional software or deterministic logic is a better solution than AI.
Reporting to the Vice President, Engineering, this role shapes enterprise technical direction, establishes reference architectures and engineering standards, advises executive and engineering leadership, represents AssistRx capabilities with clients, partners, and auditors, and develops the next generation of technical leaders. It is accountable for quality, security, reliability, performance, maintainability, cost efficiency, and the safe and compliant use of AI with protected health information across the enterprise.
ResponsibilitiesEnterprise Strategy and Architecture
Define the multi-year technical strategy for AI-enabled software in partnership with Engineering, Product, and Architecture, aligning investments to business value, feasibility, risk, and total cost of ownership.
Lead the architecture and delivery of the most complex enterprise-scale solutions spanning multiple products, clients, platforms, and technology stacks.
Set the enterprise standard for when to use GenAI, predictive models, intelligent document processing, conventional application logic, workflow automation, or a hybrid approach.
Own reference architectures and engineering standards for web applications, APIs, distributed services, integrations, data flows, model access, retrieval services, prompt and version management, evaluation, observability, security, and resilience.
Identify emerging technologies and architectural patterns that create competitive advantage, and lead proofs of concept through production-scale adoption when evidence supports the investment.
Full-Stack Engineering and Delivery
Remain hands-on in the design, implementation, review, troubleshooting, and production hardening of critical solutions across frontend, backend, cloud, integration, data, and AI layers.
Ensure AI capabilities are embedded in coherent, usable, secure, and supportable applications rather than delivered as isolated model or service components.
Lead the design of modern web experiences, REST APIs, event-driven and distributed services, enterprise integrations, identity and access patterns, and cloud infrastructure where required by the initiative.
Resolve systemic technical constraints and cross-team dependencies, and create platform capabilities that allow multiple teams to deliver safely and consistently.
Set standards for automated testing, CI/CD, infrastructure as code, observability, performance engineering, incident learning, and production readiness across AI-enabled solutions.
Applied AI and Responsible AI
Architect enterprise LLM applications using retrieval-augmented generation, structured outputs, prompt design, function and tool calling, agent-based orchestration, and appropriate model-selection strategies.
Set patterns for conversational AI, voice experiences, assistants, intelligent document workflows, and agentic systems, including state management, routing, human review, bounded autonomy, and safe fallbacks.
Define enterprise evaluation standards for quality, grounding, safety, bias, privacy, latency, reliability, and cost;
ensure regression testing and monitoring are integrated into the delivery lifecycle.
Establish responsible AI practices appropriate for protected health information and high-impact healthcare workflows in partnership with Security, Compliance, Legal, and business owners.
Set the standard for design documentation of AI features, covering intended behavior, context and retrieval design, prompt and agent structure, evaluation approach, and fallback behavior
Anticipate scaling, reliability, and cost challenges in AI systems, and lead architectural investments that address them before they affect clients
Evaluate models, platforms, and vendors;
make build-versus-buy recommendations and guide commercial and technical decisions based on evidence.
Set the standard for design documentation of AI features, covering intended behavior, context and retrieval design, prompt and agent structure, evaluation approach, and fallback behavior
Run structured experiments and A/B tests across prompts, retrieval configurations, model selection, and workflow design, and use the results to drive iteration
Establish and enforce quality gates and regression thresholds so AI features must meet defined accuracy and safety criteria before release
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
Document model design, data sources, intended use, and known limitations to support internal AI governance, audits, and client due diligence
Organizational Technical Leadership
Serve as a trusted technical advisor to the Vice President, Engineering and other leaders on architecture, investment, risk, staffing, and delivery strategy.
Influence roadmaps across Product, Architecture, Data, Security, Compliance, and Client Services and clarify trade-offs for executive and client audiences.
Lead architecture reviews and technical decision forums for the highest-risk initiatives, and intervene directly when delivery, reliability, security, or maintainability is at risk.
Develop Staff and Senior engineers through mentoring, sponsorship, design reviews, and visible hands-on leadership.
Partner with Product Management and business stakeholders to prioritize work by business value, feasibility, risk, and total cost of ownership.
Represent AssistRx technical capabilities with clients, partners, vendors, and auditors when senior technical leadership is required.
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;
Master’s degree preferred.
10+ years of professional software engineering experience, including substantial experience building and operating distributed production systems and delivering AI or machine learning capabilities in production.
Demonstrated principal-level architecture and technical leadership across multiple products, teams, platforms, or business domains.
Deep full-stack engineering experience covering modern web applications, REST and event-driven services, data access, system integration, cloud infrastructure, automated testing, deployment, observability, and production operations.
Expert-level capability in at least one general-purpose application development language and strong production experience in additional languages, such as C#, Java, Python, TypeScript/JavaScript, Go, or comparable technologies.
Experience with a modern frontend framework such as React, Angular, Vue, or a comparable framework, including the architectural concerns of production user-facing applications.
Deep hands-on expertise with LLM and GenAI patterns, including RAG, structured outputs, prompt and context design, tool/function calling, agents, evaluation, guardrails, monitoring, and cost management.
Experience evaluating model quality, cost, latency, security, privacy, and operational risk in production AI systems.
Expertise in cloud architecture, distributed systems, containers, CI/CD, infrastructure as code, application security, reliability, performance optimization, and technical risk management.
Demonstrated ability to make technology-agnostic architectural decisions and to operate effectively outside a historical language or platform specialization.
Executive-level communication skills and a record of influencing technical direction without relying on direct organizational authority.
Preferred Qualifications
Advanced degree in Computer Science, Engineering, Data Science, or a related technical field.
Experience in healthcare technology, specialty medication access, protected health information, or another highly regulated environment.
Experience defining enterprise architecture or platform strategy for AI-enabled software across multiple product teams.
Experience with conversational AI, voice applications, intelligent document processing, predictive models, or agentic workflows at enterprise scale.
Experience supporting client, partner, regulatory, compliance, or audit discussions involving production technology and AI risk.
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
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
Pay RangeUSD $163,239.00 - USD $204,048.00 /Yr.
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