AI Engineer V (Principal)

AssistRx, LLC; Miami (FL); On-site; USD 162,000 - 204,000; Full time

Job summary

AssistRx, LLC seeks an AI Engineer V (Principal) to define enterprise AI strategy and lead the design, build, and deployment of production-grade AI solutions within a HIPAA-compliant healthcare ecosystem.

You will set architecture standards, mentor engineers, and partner with leadership to drive responsible, cost-effective AI across platforms, products, and clients.

Qualifications

• Bachelor’s degree in CS or related field; Master’s preferred.
• 10+ years software engineering, including 6+ years AI in production.
• Experience delivering enterprise AI strategy for real customers.

Responsibilities

• Define multi-year AI strategy and roadmaps in partnership with senior leadership.
• Lead design of enterprise-scale AI and ML solutions across platforms and clients.
• Architect LLM applications using retrieval-augmented generation and tool calls.
• Set patterns for agentic workflows, memory, and fallback paths for high-impact decisions.
• Evaluate AI platforms and vendor tools; decide build vs. buy for value and risk.
• Guide responsible AI practices ensuring HIPAA compliance and data security.

Skills

Python

AI/ML engineering

Distributed systems

Leadership

Regulatory/compliance knowledge

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Master’s degree preferred

Tools

AWS

Azure

Git

Docker

Kubernetes

LangChain

Job description

The AI Engineer V (Principal) role at AssistRx, LLC is the most senior individual-contributor technical authority for AI and machine learning. This position defines and drives enterprise AI strategy while leading the design, build, and deployment of production-grade AI solutions, with a focus on safe and compliant use of AI in a specialty medication access ecosystem that involves protected health information.

Role Overview

In partnership with senior leadership across Engineering, Product, and Architecture, this role sets enterprise AI architecture, evaluation standards, and responsible AI practices. The Principal engineer leads complex AI solution delivery spanning generative AI, predictive modeling, and intelligent document processing, ensuring systems meet reliability, quality, and compliance expectations.

Key Responsibilities

• Define the multi-year AI technical strategy and roadmap in partnership with the Vice President, Engineering, Product, and Architecture, aligning AI investments to business value, feasibility, and risk.
• Lead design of enterprise-scale AI and machine learning solutions, including generative AI, predictive models, and intelligent document processing across platforms, products, and clients.
• Architect large language model applications using retrieval augmented generation, prompt engineering, structured outputs, function and tool calling, and agent-based orchestration.
• Set patterns for agentic workflows, including task decomposition, tool and function calling, memory and state management, routing, human review checkpoints, boundaries, and fallback paths for high-impact decisions such as benefits verification and prior authorization.
• Identify new AI opportunities and emerging technologies, and lead proofs of concept that transition proven pilots to scale.
• Determine the right approach per problem, including prompting, retrieval augmentation, tool use, task-specific models, and deterministic logic when models are not appropriate.
• Set the standard for AI feature design documentation, covering intended behavior, context and retrieval design, prompt and agent structure, evaluation approach, and fallback behavior.
• Own the enterprise AI reference architecture, platform strategy, and engineering standards, including model access, prompt and version management, and retrieval services, and ensure adoption across engineering teams.
• Lead evaluation of commercial models, AI platforms, and vendor tools, and recommend build versus buy to executive leadership based on quality, cost, security, strategic fit, and time to value.
• Guide vendor and cloud provider relationships technically, including model selection, capacity, pricing, and risk.
• Anticipate scaling, reliability, and cost challenges, and drive architectural investments to address them before client impact.
• Set organization-wide targets for accuracy, groundedness, hallucination rate, task completion, latency, and cost, guiding model selection, tuning, and evaluation.
• Define evaluation strategy, benchmark datasets, and automated test frameworks to measure output quality and detect regressions before release.
• Optimize retrieval pipelines, embeddings, chunking strategies, and inference performance to improve quality and reduce cost per transaction at scale.
• Direct error analysis and human-in-the-loop review programs with operations and clinical subject matter experts, ensuring improvements flow into prompts, models, and guardrails.
• Establish experimentation practices including A/B testing across prompts, retrieval configurations, model selection, and workflow design.
• Define and enforce quality gates and regression thresholds so AI features meet defined accuracy and safety criteria before release.
• Set technical direction for pipelines that prepare structured and unstructured healthcare data (such as enrollment forms, payer documents, and clinical notes) for training, retrieval, and inference.
• Partner with Data Engineering and Architecture to define enterprise data contracts, feature sets, vector stores, and lineage for AI workloads.
• Define enterprise standards for de-identification, masking, and data minimization when working with protected health information.
• Define enterprise standards for CI/CD, source control, containerization, and infrastructure-as-code practices for AI workloads.
• Establish versioning, promotion, and rollback practices for prompts, agents, and model configurations as versioned artifacts alongside application code.
• Set observability strategy for accuracy drift, groundedness, task completion, token consumption, latency, cost, and reliability, including alerting and incident response practices.
• Own strategy for model and inference spend, applying caching, routing, and model right-sizing to keep AI solutions cost-effective at scale.
• Serve as final technical escalation point for critical production issues and client-reported defects in AI-enabled features, and drive systemic improvements.
• Lead responsible AI approach to satisfy healthcare privacy, data security, and regulatory requirements including HIPAA and HITRUST.
• Partner with Security, Compliance, and Legal to address AI-specific risks (prompt injection, data leakage, insecure output handling) and confirm vendor agreements (including Business Associate Agreements) prior to PHI processing.
• Define guardrails, content filtering, human oversight, and audit trails appropriate to each use case risk profile.
• Contribute to internal AI governance and represent AssistRx’s AI practices in audits, client due diligence, and security reviews.
• Track emerging AI regulation and industry guidance, and translate it into engineering policy and practice.
• Serve as trusted technical advisor to the Vice President, Engineering and executive leadership on AI strategy, risk, investment, and capability.
• Provide technical leadership across the engineering organization by influencing architecture and engineering direction at the enterprise level without direct authority.
• Mentor and develop Staff, Senior, and mid-level engineers, build the AI technical bench, and raise AI fluency across engineering and the broader company.
• Lead cross-functional technical initiatives and resolve complex technical disagreements across teams.
• Work with Product Management, Client Services, and business leaders to identify high-value AI use cases and define success measures before build begins.
• Communicate AI capabilities, limitations, risks, and results in plain language to technical and non-technical audiences, including executives, clients, and external partners.
• Contribute to hiring strategy, interviewing, and role definition for AI and related engineering roles.
• Advance AI-assisted and agentic development practices including specification-driven workflows and agentic coding tools, sharing what works with engineering peers.
• Participate in agile team activities as needed, including sprint reviews and retrospectives, and conduct regular self-guided study to stay current with AI advances.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field (or equivalent experience); Master’s degree preferred.
• 10+ years of software engineering experience, including 6+ years building and deploying AI or machine learning solutions in production.
• Track record of leading enterprise-scale AI strategy and delivery for real customers or operations teams, with measurable business outcomes.
• Expert programming skills in Python, with experience in .NET/C#/Java or a comparable enterprise language, including backend services and REST APIs (such as FastAPI, Flask, or ASP.NET).
• Deep hands-on expertise with large language models and generative AI patterns, including retrieval augmented generation, prompt engineering, embeddings, vector and hybrid search, reranking, structured outputs, function and tool calling, context window management, and model evaluation.
• Extensive experience architecting and operating production AI workloads on cloud-based AI and machine learning platforms, with AWS preferred (Amazon Bedrock, Amazon SageMaker); equivalent Azure experience is acceptable (Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning).
• Expert knowledge of APIs, microservices, distributed systems, SQL, and modern development practices including Git, CI/CD, containerization, and automated testing.
• Demonstrated ability to deliver in environments shaped by data privacy, security, and regulatory requirements.
• Demonstrated enterprise-level technical leadership, including setting technical strategy and developing technical leaders.
• Ability to work independently on highly ambiguous, high-impact problems, setting direction and making sound tradeoffs with limited precedent.
• Hands-on experience building software with agentic coding tools such as Claude Code, GitHub Copilot agents, Codex, or Cursor, including specification-driven workflows and multi-step agent execution.
• Deep practical understanding of LLM behavior in production, including hallucination, prompt sensitivity, non-determinism, and latency and cost tradeoffs.
• Expert knowledge of evaluation methods for LLM systems, including automated scoring, LLM-as-judge techniques, and human review workflows.
• Exceptional written and verbal communication skills, able to influence executives, clients, and engineers and represent the company’s AI approach externally.

Preferred Qualifications

• Master’s or doctoral degree in Computer Science, Machine Learning, Data Science, or related field.
• Experience in healthcare technology, specialty pharmacy, HUB services, ePA, or EHR/EMR integrations.
• Experience with intelligent document processing, OCR, and data extraction from unstructured enrollment or clinical documents.
• Applied machine learning or data science background, including experiment design, model evaluation, and error analysis.
• Experience with Docker, Kubernetes, or infrastructure-as-code at scale.
• 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 establishing an AI function or serving as the senior AI technical leader for an organization.
• Experience representing technical capabilities in client due diligence, security reviews, or external audits.

Technologies

• Python; .NET; C#; Java; FastAPI; Flask; ASP.NET
• Retrieval augmented generation; prompt engineering; structured outputs; function and tool calling; agent-based orchestration
• Embeddings; vector search; hybrid search; reranking; context window management; LLM-as-judge
• AWS (Amazon Bedrock, Amazon SageMaker) and Azure (Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning)
• APIs; microservices; distributed systems; SQL; Git; CI/CD; containerization; automated testing
• Claude Code; GitHub Copilot agents; Codex; Cursor

Location and Compensation

• Location: Florida (onsite)
• Salary: USD 162,239 - 204,048 per yearly