Senior AI Engineer/AI Platform Developer

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 qualification
• Customer service
• Drug-testing workflows
• Background and employment-screening workflows
• Compliance review
• Document analysis
• Telehealth operations
• Order processing
• Revenue-cycle workflows
• Financial analysis
• Employee training
• Internal knowledge management
• Executive 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 architecture
• Prototyping
• Full-stack development
• Backend engineering
• API integrations
• AI model integration
• Database architecture
• Cloud deployment
• CI/CD
• Testing
• Monitoring
• Documentation
• Production 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:

• HubSpot
• Shopify
• WooCommerce
• Background-screening platforms
• Drug-testing systems
• Telehealth platforms
• Laboratory systems
• Payment systems
• ERP/accounting platforms
• Internal databases
• Third-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 controls
• Audit logging
• Encryption
• Secure API design
• Data isolation
• Human-in-the-loop controls
• AI output validation
• Model evaluation
• Hallucination/error monitoring
• PII/PHI protection
• Vendor 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:

• Python
• TypeScript / JavaScript
• FastAPI, Node.js, or comparable backend frameworks
• REST APIs and webhooks
• SQL and relational databases
• PostgreSQL
• Redis
• Vector databases
• Docker
• Git/GitHub
• Cloud architecture: AWS, Azure, or GCP
• CI/CD
• Production monitoring and observability

Strong experience with modern AI technologies including:

• OpenAI
• Anthropic
• Gemini
• Open-source LLMs
• RAG
• Embeddings
• Vector search
• Structured outputs
• Function/tool calling
• Agentic architectures
• Prompt engineering
• AI evaluations
• Model benchmarking
• Fine-tuning where appropriate
• AI guardrails and safety systems

Experience 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 Engineer
• Staff AI Engineer
• Founding AI Engineer
• Machine Learning Engineer
• AI Platform Engineer
• Senior Full-Stack Engineer specializing in AI
• Applied 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 experience
• 3 years building ML/AI applications
• Demonstrated production LLM experience
• Strong computer science or engineering fundamentals
• Bachelor's or Master's degree in Computer Science, Engineering, AI, Mathematics, or comparable discipline

Exceptional demonstrated ability can outweigh formal education requirements.

What We Want to See

Candidates should be prepared to demonstrate:

1. Production AI systems personally designed or built.
2. Architecture decisions and tradeoffs.
3. AI agents or workflows they have deployed.
4. Measurable business impact.
5. Code quality and engineering practices.
6. How they evaluate LLM accuracy and reliability.
7. 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 production
• Hours of manual work eliminated
• Revenue influenced or generated by AI
• Reduction in operating costs
• User adoption
• System uptime and reliability
• Model accuracy
• Reduced AI error rates
• Faster business processes
• Successful integrations
• AI 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.