Role overview
We are seeking an AI Engineer with hands-on experience in open-source language models, local inference, RAG systems, agent architectures, function/tool calling, MCP, and end-to-end data pipelines.
This role requires someone who can:
● Understand business processes deeply,
● Communicate effectively with non-technical team members,
● Architect AI solutions that are stable, scalable, and actually useful in day-to-day operations.
This is a design → build → deploy → iterate role where your work will directly impact core business workflows.
Responsibilities
● Design, build, and deploy AI-powered tools and assistants that support clinical, staffing,
scheduling, analytics, and administrative workflows.
● Work with open-source LLMs (LLaMA, Mistral, Gemma, etc.) and local inference runtimes
(Ollama, vLLM, Text Generation Inference).
● Implement RAG pipelines using embeddings, vector databases (Chroma, Qdrant, Weaviate, pgvector), and retrieval heuristics tailored to business context.
● Build multi-tool / function-calling agents, including execution planning, state management, and iterative reasoning flows.
● Architect and integrate MCP-based agents with internal systems, CRMs, databases, analytics dashboards, and forms/workflows.
● Develop and maintain data pipelines for ingestion, cleaning, semantic indexing, embeddings, storage, and scheduled refresh.
● Deploy models and pipelines both locally and in the cloud (AWS, containerized GPU servers, macOS AI compute environments).
● Optimize inference performance, caching, batching, routing, and cost vs. latency trade-offs.
● Collaborate directly with non-technical staff to gather requirements and translate real operational needs into practical AI tools.
● Document workflows, maintain best practices, and train internal users on effective tool usage.
Required Skills & Experience
AI / ML / LLM
● Hands-on deployment of open-source models (fine-tuned or instruct-tuned models a plus).
● Strong understanding of vector search, embeddings, context window strategies, and RAG best
practices.
● Experience building agent architectures with structured function/tool calling.
● Familiarity with MCP, LangGraph, LlamaIndex, LangChain, or similar orchestration frameworks.
Software & Systems
● Strong Python experience; familiar with Django / FastAPI / Flask or similar frameworks.
● Experience building data pipelines (ETL/ELT, semantic chunking, scheduled indexing).
● Experience deploying AI systems: Docker, AWS EC2 / ECS / Lambda, GPU instances, or local inference stacks.
● Comfort with monitoring, logging, and performance optimization.
Communication & Business Understanding
● Ability to understand business workflows, not just code.
● Can explain complex technical ideas simply and clearly.
● Works directly with end-users and adapts tools based on feedback.
Preferred qualifications
● Healthcare operations / scheduling / staffing workflow familiarity.
● Knowledge of HIPAA and security practices around PHI/PII.
● Experience with Apple Silicon GPU/ML workloads (e.g., Mac Studio-based compute clusters).
Benefits
● A mission-driven environment where your work has real-world impact.
● Ownership of architecture decisions and implementation.
● A collaborative team that values clarity, performance, and craftsmanship.
● Room to experiment, iterate, and build production-quality tools quickly.
Job Type: Full-time
Pay: $100,000.00 - $160,000.00 per year
About the company
We are a data-driven organization building secure, modern systems that power clinical, operational, and administrative workflows. Our next stage of growth involves integrating practical, production-grade AI into daily business operations.
We value clarity, ownership, thoughtful design, and measurable impact.