AI Engineer IV (Staff)

AssistRx, LLC; Miami (FL); Presencial; USD 135 000 - 169 000; Tempo integral

Resumo da oferta

AssistRx, LLC in Florida is seeking a senior AI engineer to lead design, build, and deployment of production-grade AI solutions across the specialty medication access ecosystem. This onsite role will set AI technical direction, own high-risk AI systems, and ensure quality, performance, cost efficiency, and compliant handling of PHI.

You will drive AI architecture, APIs, and workflows, shaping the roadmap with Product and Architecture, and mentor engineers while collaborating across teams to

Qualificações

• 7+ years of software engineering experience, incl 4+ years building and deploying AI/ML in production.
• Strong programming skills in Python, with experience in .NET/C#/Java and REST APIs.
• Track record leading and shipping multiple AI features used by real customers or operations teams.
• Hands-on experience with LLMs and generative AI patterns (RAG, embeddings, evaluation).
• Experience deploying production AI workloads on cloud platforms (AWS/Azure).

Responsabilidades

• Lead design, development, and deployment of complex multi-system AI solutions.
• Architect applications on large language models using retrieval augmented generation and prompt engineering.
• Design agentic workflows with tool calling, memory/state management and human review checkpoints.
• Own AI reference architecture, reusable components, and engineering standards for AI development.

Conhecimentos

Python

.NET/C#/Java

LLM/GenAI

Prompt engineering

API design

CI/CD

Cloud platforms

Agentic coding tools

Leadership

Security/Compliance

Formação académica

Bachelor’s degree in Computer Science/Engineering/Data Science or related field

Master’s degree in CS/ML/Data Science or related field

Ferramentas

Claude Code

GitHub Copilot agents

Codex

Cursor

Descrição da oferta de emprego

AssistRx is seeking a senior, staff-level AI Engineer to lead the design, build, and deployment of production-grade AI solutions across the specialty medication access ecosystem. In this onsite role in Florida, you will set AI technical direction, own high-risk AI systems, and ensure quality, performance, cost efficiency, and compliant handling of PHI.

Responsibilities

• Lead the design, development, and deployment of complex multi-system AI and machine learning solutions aligned with 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, scaling proven pilots across products and clients.
• Select appropriate solution approaches, including prompting, retrieval augmentation, tool use, task-specific models, and conventional deterministic logic when a model is not the right answer.
• Author and review design documentation for AI features, including intended behavior, context and retrieval design, prompt and agent structure, evaluation approach, and fallback behaviour.
• Own the AI reference architecture, reusable components, and engineering standards for AI development, including model access, prompt and version management, and retrieval services.
• 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 with Product and Architecture by prioritizing work based on 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 investments to address them.
• Select, tune, and evaluate models against targets for accuracy, groundedness, hallucination rate, task completion, latency, and cost.
• Define evaluation frameworks, benchmark datasets, and automated test suites to 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, and workflow design.
• Establish and enforce quality gates and regression thresholds so AI features meet defined accuracy and safety criteria before release.
• Define pipelines to prepare structured and unstructured healthcare data (for example enrollment forms, payer documents, and clinical notes) for training, retrieval, and inference.
• Partner with Data Engineering to define data contracts, feature sets, vector stores, and lineage for AI workloads.
• Set standards for de-identification, masking, and data minimization when working with protected health information.
• Set standards for CI/CD, source control, containerization, and infrastructure-as-code practices for AI workloads, and maintain versioning, promotion, and rollback practices for prompts, agents, and model configurations.
• Define monitoring for accuracy drift, groundedness, task completion, token consumption, latency, cost, and reliability through logging, observability, and alerting.
• Track and manage model and inference spend, applying caching, routing, and model right-sizing to keep AI solutions cost-effective at scale.
• Serve as the escalation point for complex production issues and client-reported defects in AI-enabled features, leading root cause analysis and continuous improvement.
• Build solutions that satisfy healthcare privacy, data security, and regulatory requirements including HIPAA and HITRUST.
• Partner with Security and Compliance on AI-specific risks such as prompt injection, data leakage, and insecure output handling, and ensure vendor agreements (including Business Associate Agreements) are in place before PHI is processed.
• Define guardrails, content filtering, human oversight, and audit trails appropriate to each use case’s risk profile, and document model design, data sources, intended use, and known limitations to support internal governance and audits.
• Provide technical leadership across multiple teams without direct authority, influence engineering direction, and mentor Senior and mid-level engineers.
• Lead design reviews and peer code reviews, contribute to shared standards and reference architectures, and support day-to-day agile activities including sprint planning, daily standups, sprint reviews, and retrospectives.
• Apply and help improve AI-assisted and agentic development practices, including specification-driven workflows and agentic coding tools, and share effective approaches.
• Conduct self-guided study to stay current in a fast-moving AI landscape and bring relevant techniques and tooling to the team.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field (or equivalent experience).
• 7+ years of software engineering experience, including 4+ years building and deploying AI or machine learning solutions in production.
• Track record of leading and shipping multiple AI features used by real customers or operations teams, with measurable business outcomes.
• Strong programming skills in Python, with experience in .NET/C#/Java or a comparable enterprise language, including backend services and REST APIs (for example FastAPI, Flask, or ASP.NET).
• Hands-on experience with large language models and generative AI patterns, including RAG, prompt engineering, embeddings, vector and hybrid search, reranking, structured outputs, function and tool calling, context window management, and model evaluation.
• Experience designing and deploying production AI workloads on cloud-based AI and machine learning platforms (AWS preferred, including Amazon Bedrock and Amazon SageMaker; equivalent Azure experience acceptable).
• Strong knowledge of APIs, microservices, SQL, and modern development practices including Git, CI/CD, containerization, and automated testing.
• Ability to deliver in environments where data privacy and security requirements shape solution design.
• Demonstrated technical leadership across teams, including setting technical direction, architectural decisions, and mentoring engineers.
• Ability to work independently on ambiguous, high-impact problems, set direction, and make 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.
• Practical understanding of LLM behavior in production, including hallucination, prompt sensitivity, non-determinism, and latency and cost tradeoffs.
• Strong knowledge of evaluation methods for LLM systems, including automated scoring, LLM-as-judge techniques, and human review workflows.
• Excellent written and verbal communication skills, able to influence and collaborate with engineers, executives, and clients in a fast-paced agile environment.

Preferred Qualifications

• Master’s 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.
• Experience with agent and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Strands, or Model Context Protocol.
• Experience with MLOps 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.

Technologies

• Python, .NET, C#, Java
• FastAPI, Flask, ASP.NET
• Retrieval augmented generation, prompt engineering, embeddings, vector search, hybrid search, reranking, structured outputs, function calling, tool calling, context window management, model evaluation
• AWS (Amazon Bedrock, Amazon SageMaker) and Azure (Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning)
• APIs, microservices, SQL, Git, CI/CD, containerization, automated testing
• Claude Code, GitHub Copilot agents, Codex, Cursor
• A/B tests
• HIPAA, HITRUST

Pay Range

USD 135,051 - 168,814 per year

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