An Agentic AI Engineer at Assort Health sits at the absolute frontier of healthcare technology. In this role, you will build, scale, and optimize autonomous AI agents capable of navigating the immense complexity of medical administrative workflows. These agents do not simply generate text; they act as intelligent, voice-enabled medical receptionists and clinical coordinators that interact with patients, integrate deeply with Electronic Health Records (EHR), manage scheduling, and handle billing processes.

The impact of your work is direct and profound. By automating the administrative friction that plagues the healthcare system, your agents help eliminate medical staff burnout, reduce operational overhead, and ensure that patients receive immediate, friction-free access to care. You will work on problems where deterministic execution meets non-deterministic natural language, ensuring that AI agents make safe, accurate, and HIPAA-compliant decisions in real time.

This position is technically demanding because it requires a unique blend of software engineering rigor and cutting-edge AI orchestration. Whether you join as an Agent Software Engineer, a Senior Agent Software Engineer, or a Staff Agent Software Engineer, you will be responsible for designing state machines, optimizing speech-to-text (STT) and text-to-speech (TTS) pipelines, and building robust error-handling frameworks. You will build systems where latency is measured in milliseconds and reliability is a matter of patient health.

The questions you will encounter during the Assort Health interview process are designed to evaluate your practical engineering skills, your understanding of large language model (LLM) limitations, and your ability to architect reliable agentic systems. These questions are drawn from real candidate experiences and represent the core challenges you will face on the job.

Agentic Architecture & LLM Orchestration

These questions evaluate your ability to design systems where LLMs interact with external tools, maintain state, and recover from failures gracefully.

• How do you design a state machine for an AI agent that must handle a multi-step patient intake process while allowing the patient to ask clarifying questions out of order?
• What strategies do you use to mitigate prompt injection and ensure an agent does not execute unauthorized tool calls?

Preparing for an interview at Assort Health requires a shift away from traditional competitive programming toward practical systems engineering and agent architecture. You must demonstrate that you can build systems that are both highly flexible and strictly reliable.

Agentic Architecture and State Management – You must show a deep understanding of how to structure agent memory, manage conversation state, and handle multi-turn context. Be prepared to explain how you construct robust system prompts and guide agent trajectories.

Systems Engineering & Latency Control – In voice-based healthcare applications, latency is the ultimate killer of user experience. You should be ready to discuss asynchronous programming, WebSockets, streaming APIs, and efficient data serialization.

Safety, Security, and Compliance – Working in healthcare means dealing with Protected Health Information (PHI). You must demonstrate an understanding of security best practices, HIPAA compliance, and how to build deterministic guardrails around non-deterministic model outputs.

Pragmatic Problem Solving – Assort Health values engineers who build simple, maintainable solutions rather than over-engineered systems. Show that you know when to use simple heuristics or regex instead of spinning up an expensive LLM call.

The interview process at Assort Health is thorough, highly technical, and deeply aligned with the day-to-day realities of building production-grade AI agents. The team moves quickly, valuing clear communication, technical depth, and a collaborative mindset.

You will begin with an initial screen focused on your background, your experience with generative AI, and your interest in healthcare technology. From there, you will move into technical evaluations that assess your coding proficiency and your architectural thinking. The process culminates in a comprehensive onsite loop where you will interact with cross-functional team members, including engineering leaders and product managers.

The timeline above outlines the typical progression from your first conversation to a final offer. Candidates should use this roadmap to pace their preparation, focusing first on core coding and agentic fundamentals before diving deep into complex system design and behavioral scenarios. The entire process is designed to be highly transparent, giving you a clear window into the engineering culture at Assort Health.

LLM Orchestration & Agentic Workflows

This is the core of the technical evaluation. The interviewers want to see if you understand how to move beyond simple single-prompt inputs and build complex, looping, tool-using systems.

Be ready to go over:

• Tool Calling & Function Selection – How to structure JSON schemas for tool definitions and ensure the model selects the correct tool with the correct arguments.
• Memory Architectures – Managing short-term conversational memory versus long-term semantic memory using vector databases.
• Error Recovery – Strategies for when an LLM returns malformed JSON or calls a tool that does not exist.
• Advanced concepts (less common) – Multi-agent collaboration frameworks, hierarchical routing, and self-reflection loops where an agent critiques its own output before responding.

Example questions or scenarios:

• "Design an agent that can look up a patient's insurance benefits, verify copay details, and write a summary back to the clinic's database, handling cases where the insurance API returns partial matches."
• "How would you implement a fallback mechanism when an LLM fails to generate a valid tool call after three consecutive attempts?"

Real-Time Systems & Latency Optimization

At Assort Health, voice agents must respond naturally to patients over the phone. This requires an exceptionally fast, event-driven architecture.

Be ready to go over:

• Asynchronous Execution – Utilizing concurrency frameworks like asyncio in Python to handle multiple I/O-bound tasks simultaneously.
• Streaming Architectures – Streaming LLM tokens directly into text-to-speech engines to minimize Time-to-First-Byte (TTFB).
• Voice Activity Detection (VAD) – Handling user interruptions gracefully in a duplex audio stream.
• Advanced concepts (less common) – Custom audio codec compression, WebRTC implementations, and optimizing network hops between telephony carriers and AI pipelines.

Example questions or scenarios:

• "Walk through the entire lifecycle of a voice packet from the moment a patient speaks to the moment the AI agent responds. Identify every source of latency and how you would optimize it."
• "How do you handle a situation where a patient starts speaking while the AI agent is in the middle of generating a spoken response?"

Determinism & Safety in Healthcare AI

Because Assort Health operates in the clinical domain, building guardrails to prevent harmful or incorrect behavior is paramount.

Be ready to go over:

• Input/Output Guardrails – Implementing validation layers to filter out sensitive or unsafe content before it reaches the model or the user.
• Deterministic Routing – Using traditional software engineering patterns to handle critical pathways (like emergency routing) instead of relying on LLM decisions.
• Structured Outputs – Enforcing strict output formats using libraries like Pydantic or instructor.
• Advanced concepts (less common) – Fine-tuning smaller, open-source models to perform highly specific classification tasks with 100% deterministic formatting.

Example questions or scenarios:

• "A patient calls saying they are experiencing severe chest pain. How do you design your agentic workflow to immediately detect this emergency and route the call to a human nurse without waiting for an LLM to process the query?"
• "How do you guarantee that an agent never discloses another patient's PHI during a conversational session?"