Prompt Engineer

What matters most

• Reliable customer conversations

  The role centers on designing and improving prompts for LLM applications and AI voice agents so customer conversations are natural, structured, and reliable.

• Scenario-based dialogue flows

  Design flows for different customer scenarios and test them against real-world inputs and edge cases. The agents operate across calls, WhatsApp, SMS, and other channels.

• Measurable response quality

  Identify hallucinations, repetition, incorrect behavior, and instruction-following failures; develop test cases and evaluation frameworks to assess AI responses.

• Improving agent workflows

  Experiment with prompting techniques and LLMs, including context, memory, structured outputs, tool or function calling, and conversation history; collaborate with Product and Engineering on improvements.

Likely challenges to think through

• Naturalness versus control

  Preparation hypothesis: a prompt may sound conversational yet fail to follow instructions or produce a required structure. Prepare an example of how you would define both quality criteria and test revisions against each.

• Coverage versus testing effort

  Preparation hypothesis: varied customer inputs and edge cases could outnumber practical tests. Prepare a small, prioritized test set covering typical scenarios and the failure types named in the posting, with a clear way to compare prompt versions.

• Continuity across complex interactions

  Preparation hypothesis: using conversation history, memory, and tool calls may make it harder to keep responses consistent and correct. Prepare to explain how you would test a multi-turn flow, including what should happen when context is missing or a tool result is unexpected.

Topics to prepare

• Prompt reliability

  The role calls for natural, structured, reliable LLM conversations. Revision exercise: explain how a prompt can specify the agent’s task, response boundaries, and behavior when information is missing.

• Dialogue-flow design

  The posting names dialogue flows for different customer scenarios. Revision exercise: sketch the decision points for a customer inquiry, including a clarification, an interruption, and a return to the original request.

• AI response failure modes

  The role explicitly targets hallucinations, repetition, incorrect behavior, and instruction-following issues. Revision exercise: define each failure and write one short example that would reveal it.

• Evaluation principles

  The posting calls for test cases and evaluation frameworks. Revision exercise: define observable criteria for whether a customer-facing response is accurate, relevant, and appropriately structured.

• Context and conversation state

  The role mentions context, memory, and conversation history. Revision exercise: decide which facts from a short multi-turn exchange an agent should retain, update, or stop using.

• Structured outputs and function calling

  Both are named in the posting. Revision exercise: describe when a response should follow a specified format and when a function call would be more appropriate than a conversational answer.

Skills to revise

• Revise a customer-conversation prompt

  Suggested practice: write a prompt for a hypothetical customer inquiry, test it on several inputs, and revise wording that produces unclear or repetitive replies. This mirrors the advertised prompt-design and optimization work.

• Build a branching dialogue

  Suggested practice: draft a short voice-agent flow with a clear opening, clarification path, and recovery from a misunderstood answer. The posting specifically calls for dialogue flows and AI voice agents.

• Create an edge-case test set

  Suggested practice: write inputs with missing details, conflicting requests, and abrupt topic changes; record the expected behavior for each. The role requires testing real-world inputs and edge cases.

• Evaluate and diagnose responses

  Suggested practice: score sample replies against a small rubric, label any hallucination or instruction-following failure, and propose one prompt change to test. This reflects the posting’s evaluation and continuous-improvement responsibilities.

• Check format and function-call behavior

  Suggested practice: specify an output format and a hypothetical function-call condition, then inspect sample replies for format errors or inappropriate calls. The posting explicitly mentions structured outputs and tool/function calling; no particular tool is specified.

• Prepare a prompt-improvement walkthrough

  Bring one project or practice example showing an initial prompt, test inputs, observed failures, revisions, and resulting responses. The role emphasizes designing, testing, evaluating, and improving prompts.

• Sketch a customer-conversation flow

  For practice, map a sample voice-agent conversation with the user's goal, follow-up questions, an edge case, and a recovery path. Be ready to explain how you would keep responses natural and structured.

• Build a small evaluation set

  Write sample customer inputs and expected behaviors, including tests for hallucinations, repetition, incorrect behavior, and instruction-following. Explain how you would compare prompt versions.

• Demonstrate context and tool handling

  Prepare a short example showing how conversation history or memory affects a response, plus a structured output or tool/function call. State what you would test when the needed information is missing.

• Explain a collaboration handoff

  Draft a concise issue report for Product and Engineering: customer scenario, prompt version, observed response, desired behavior, and a reproducible test. This is preparation for the collaboration named in the role, not a stated company process.

• Prepare accurate application answers

  Organize examples of any prompt, writing, script, or conversational-AI work you can substantiate; identify languages you are comfortable writing conversationally. Be ready to answer the form's Bengaluru on-site and relocation question accurately.

• Write questions for the employer

  Ask which customer scenarios and channels the role would prioritize, how AI responses are evaluated, and how Product and Engineering collaborate on fixes. These are questions to clarify, not stated Desible.ai practices.