AI Engineer

What matters most

• Deploy voice and chat agents

  The role explicitly calls for building and deploying both. Prepare to explain the design and deployment decisions behind an agent you built, distinguishing voice-specific considerations from chat.

• Develop LLM-based agentic solutions

  The posting names agentic solutions for enterprise use cases and hands-on GenAI/LLM experience. Prepare to describe how an agent decides what to do, uses available tools, and handles an unsuccessful step.

• Connect agents to backend systems

  Prompt engineering, APIs, integrations, and backend knowledge are stated responsibilities or requirements. Prepare a concrete account of how prompts and API interactions fit into an end-to-end workflow.

• Improve performance and scalability

  The role explicitly includes improving AI-system performance and scalability. Prepare to discuss what you measured, what you changed, and the resulting trade-offs; the posting does not specify particular metrics.

Likely challenges to think through

• Balance voice responsiveness with answer quality

  Preparation hypothesis, not a reported HealthAsyst problem: a voice agent may need to respond promptly while still producing useful answers. Be ready to explain how you would measure response time and quality, and decide when to simplify a workflow.

• Keep multi-step agents dependable

  Preparation hypothesis: giving an LLM more autonomy can make workflows flexible but harder to control when a tool call fails or returns unexpected data. Prepare an example of bounded actions, failure handling, and checks on the final response.

• Scale without losing reliability

  Preparation hypothesis: higher usage can expose slow integrations or inconsistent responses, even when an agent works in a small demonstration. Prepare to discuss how you would locate bottlenecks and test whether a performance improvement preserves useful behavior.

Topics to prepare

• LLMs and agentic solutions

  The role calls for agentic solutions using LLMs. As a revision exercise, sketch how an agent would handle a multi-step enterprise request, including when it should call an integration and how it would handle an unsuccessful response.

• Voice and chat agent behavior

  Building voice and chat agents is central to the posting. Compare how each would handle an unclear request, a follow-up question and a failed response; use the comparison to review conversational design decisions.

• RAG and NLP fundamentals

  The posting asks for a basic understanding of RAG and NLP. Explain, using a short example, when retrieval could help an agent answer a question and what can go wrong if the retrieved material is irrelevant.

• Performance and scalability

  Improving AI-system performance and scalability is an explicit responsibility. Sketch what you would measure as request volume grows for an LLM-backed agent, then explain one possible trade-off between response time and answer quality.

Skills to revise

• Python for LLM-backed agents

  The role requires Python and hands-on GenAI/LLM experience. As practice, write a small Python flow that sends a request to an LLM and handles an invalid or missing response; be ready to explain the choices you made.

• Prompt engineering

  Prompt engineering is listed as a responsibility. Draft and revise a prompt for a chat agent answering an ambiguous enterprise request, then test whether the revision makes its response more useful and consistent.

• Agent implementation and deployment

  The posting asks for experience building chatbots, voice bots or AI agents and for building and deploying voice and chat agents. Practice building a small agent interaction, then outline how you would check that it still works after deployment.

• API and backend integration

  The role explicitly mentions APIs, integrations and backend-system knowledge. Practice connecting an agent to a simple API, handling an error response and explaining how you would prevent that error from becoming a misleading user-facing answer.

• Prepare an agent project walkthrough

  The role calls for building and deploying voice or chat agents. Prepare one real or illustrative example covering the use case, Python components, LLM choices, deployment approach and what you would improve.

• Sketch an agentic solution

  For a hypothetical enterprise request, draw the agent workflow: inputs, LLM decisions, tool use, handoffs and failure handling. Label it as a design exercise, not a HealthAsyst project.

• Show how you evaluate prompts

  Prepare a small set of test prompts and expected behaviors for a voice or chat agent, plus an example of a prompt revision and how you would check whether it helped.

• Map an API integration

  Draw a simple request flow between an agent, an API and a backend system. Be ready to explain authentication, timeouts, errors and what information each component receives.

• Refresh RAG and NLP fundamentals

  Prepare a brief explanation of retrieval, context selection and grounded answers, with one example of how you would test whether retrieved information supports an agent’s response.

• Plan for performance and scale

  Outline how you would measure latency and reliability for a deployed agent, then identify practical ways to investigate bottlenecks. The posting explicitly mentions improving performance and scalability.

• Write questions for the employer

  Ask which enterprise voice and chat use cases are priorities, what backend integrations the role owns, and how the team measures agent quality in deployment.