Summary
✨ AI‑Generated
A senior AI engineering role focused on designing, building, and improving production AI platforms. The role requires expertise in modern AI architectures, large language models, cloud systems, and enterprise software development.
Highlights
Build production-grade AI systems with ownership across architecture, deployment, automation, and enterprise solutions.
Description
Position Mission
We are building a technology-driven healthcare and employment-screening organization operating across drug and diagnostic testing, background checks, employment screening, telehealth, healthcare fulfillment, and 503A/B2B healthcare services.
We are seeking an exceptional Senior AI Engineer / AI Platform Developer who can architect, build, deploy, and continuously improve production-grade AI systems that create measurable business outcomes.
This is not a research-only position and not a role for someone who primarily connects APIs.
We want a builder and owner who understands modern AI architecture, LLMs, agents, data pipelines, APIs, cloud infrastructure, security, evaluation, and enterprise software development—and can turn those capabilities into reliable products used by customers and employees.
What You Will Own
AI Architecture & Engineering
Design and build production-grade AI applications, agents, copilots, and automation systems.Architect multi-agent and agentic workflows for complex business processes.Build LLM applications using leading commercial and open-source models.Develop RAG architectures, vector search, knowledge systems, and enterprise AI search.Build structured and unstructured data ingestion pipelines.Develop reliable APIs and backend services supporting AI applications.Design systems for model routing, tool calling, memory, context management, and orchestration.Build AI evaluation, testing, observability, and monitoring frameworks.Optimize latency, accuracy, reliability, and AI inference costs.
AI Agents & Workflow Automation
Develop AI systems capable of automating or augmenting:
Sales prospecting and lead qualificationCustomer serviceDrug-testing workflowsBackground and employment-screening workflowsCompliance reviewDocument analysisTelehealth operationsOrder processingRevenue-cycle workflowsFinancial analysisEmployee trainingInternal knowledge managementExecutive reporting
The goal is not simply to introduce AI.
The goal is to create measurable improvements in revenue, productivity, accuracy, customer experience, and operating leverage.
Product Development
Work with leadership and business teams to rapidly convert business problems into deployable AI products.
Responsibilities include:
Technical architecturePrototypingFull-stack developmentBackend engineeringAPI integrationsAI model integrationDatabase architectureCloud deploymentCI/CDTestingMonitoringDocumentationProduction support
You should be comfortable taking a concept from a whiteboard conversation to a functioning production application.
Enterprise Integrations
Build integrations between AI systems and platforms such as:
HubSpotShopifyWooCommerceBackground-screening platformsDrug-testing systemsTelehealth platformsLaboratory systemsPayment systemsERP/accounting platformsInternal databasesThird-party healthcare APIs
AI Governance, Security & Compliance
Because our businesses operate in regulated environments, this position must build AI with security and compliance in mind.
Responsibilities include:
Role-based access controlsAudit loggingEncryptionSecure API designData isolationHuman-in-the-loop controlsAI output validationModel evaluationHallucination/error monitoringPII/PHI protectionVendor and model risk assessment
Experience working around HIPAA, FCRA, SOC 2, healthcare data, employment data, or other regulated environments is highly desirable.
Technical Qualifications
Strong professional experience with:
PythonTypeScript / JavaScriptFastAPI, Node.js, or comparable backend frameworksREST APIs and webhooksSQL and relational databasesPostgreSQLRedisVector databasesDockerGit/GitHubCloud architecture: AWS, Azure, or GCPCI/CDProduction monitoring and observability
Strong experience with modern AI technologies including:
OpenAIAnthropicGeminiOpen-source LLMsRAGEmbeddingsVector searchStructured outputsFunction/tool callingAgentic architecturesPrompt engineeringAI evaluationsModel benchmarkingFine-tuning where appropriateAI guardrails and safety systemsExperience with frameworks such as LangGraph, LangChain, LlamaIndex, PydanticAI, or equivalent technologies is valuable, but we care more about engineering fundamentals than dependency on a particular framework.
Ideal Candidate
You may have previously worked as a:
Senior AI EngineerStaff AI EngineerFounding AI EngineerMachine Learning EngineerAI Platform EngineerSenior Full-Stack Engineer specializing in AIApplied AI Engineer
You are someone who:
Ships quickly without sacrificing engineering discipline.Can operate with incomplete requirements.Thinks from first principles.Understands business economics as well as technology.Challenges weak technical decisions.Measures results instead of simply shipping features.Can communicate complex architecture clearly to executives.Uses AI aggressively in your own development workflow.Has personally built production AI products—not merely demos.Is comfortable owning important systems.
Preferred Experience
7+ years software engineering experience3+ years building ML/AI applicationsDemonstrated production LLM experienceStrong computer science or engineering fundamentalsBachelor's or Master's degree in Computer Science, Engineering, AI, Mathematics, or comparable disciplineExceptional demonstrated ability can outweigh formal education requirements.
What We Want to See
Candidates should be prepared to demonstrate:
Production AI systems personally designed or built.Architecture decisions and tradeoffs.AI agents or workflows they have deployed.Measurable business impact.Code quality and engineering practices.How they evaluate LLM accuracy and reliability.How they would architect AI safely around sensitive healthcare and employment information.
A GitHub portfolio, technical portfolio, live applications, or detailed architecture examples are strongly preferred.
First-Year Success Metrics
Success will be measured by outcomes including:
AI products successfully deployed into productionHours of manual work eliminatedRevenue influenced or generated by AIReduction in operating costsUser adoptionSystem uptime and reliabilityModel accuracyReduced AI error ratesFaster business processesSuccessful integrationsAI infrastructure cost efficiency
Our Standard
We are building an AI-first organization.
We want engineers who look at a 10-person manual workflow and ask:
“How could software and AI allow two exceptional people to accomplish this?”
If you want to build AI systems that directly affect healthcare, employment screening, revenue, operations, and the economics of growing businesses, we want to speak with you.