AI Engineer

What matters most

• Production-Ready Agentic Workflows

  The role prioritizes building functional AI agents that perform multi-step tasks and tool calling over experimental research. You must demonstrate the ability to design robust, reliable workflows that integrate with existing CRM and Zendesk systems to solve real-world customer service operational pain points.

• Systematic Evaluation and Quality Control

  A core priority is establishing frameworks to monitor accuracy, latency, and hallucinations. You will be expected to build test datasets and feedback loops to ensure AI performance meets business outcomes, moving beyond simple prototype development to continuous production improvement.

• End-to-End Stakeholder Collaboration

  You must bridge the gap between technical implementation and business needs by working directly with Customer Service and Automation teams. This requires translating operational problems into technical requirements and managing the full lifecycle of a project from identification to deployment.

• RAG System Optimization

  Improving retrieval quality is critical for the company's knowledge-based solutions. You need to demonstrate expertise in chunking, metadata management, and grounding to ensure that AI responses are accurate and properly cited based on internal SOPs and policies.

Likely challenges to think through

• Balancing Autonomy with Risk Management

  Building AI agents that execute multi-step tasks creates a risk of unintended actions. Prepare to discuss how you implement human-in-the-loop mechanisms, confidence thresholds, and exception handling to ensure safe, reliable automation in a customer-facing environment.

• Integration Complexity in Legacy Ecosystems

  Integrating AI with existing CRM and Zendesk platforms often involves dealing with brittle APIs and complex data structures. Prepare by highlighting your experience with REST APIs, JSON, and backend frameworks like FastAPI to ensure seamless communication between your AI logic and the company's operational infrastructure.

• Defining Success for Non-Deterministic Systems

  Moving from rule-based automation to AI-driven decisioning makes performance measurement difficult. You may face challenges in defining clear metrics for 'intent accuracy' or 'task completion.' Prepare by explaining how you design evaluation frameworks that provide actionable insights rather than just raw logs.

Topics to prepare

• Agentic Workflow Design

  Focus on designing multi-step task execution where an AI agent must reason, select tools, and handle exceptions. Exercise: Draft a flowchart for a customer service agent that retrieves order status via API, checks a refund policy, and decides whether to escalate to a human based on a confidence threshold.

• Production AI Evaluation Frameworks

  Understand how to measure system performance beyond simple accuracy. Exercise: Define a set of metrics to monitor for a RAG system, specifically focusing on hallucination rates, retrieval latency, and business-specific outcomes like task completion rate.

• RAG Grounding and Retrieval Quality

  Study techniques to improve the relevance of retrieved knowledge. Exercise: Compare different chunking strategies and metadata tagging methods for a set of internal SOP documents to see how they impact the precision of LLM-generated answers.

Skills to revise

• FastAPI and REST API Integration

  Build robust backend services that connect LLMs to external systems. Exercise: Create a FastAPI endpoint that accepts a JSON payload, triggers a function call to an external CRM API, and returns a structured response to an AI agent.

• Function and Tool Calling

  Implement structured outputs to allow LLMs to interact with software. Exercise: Develop a Python script using an LLM API that defines a set of tools (e.g., get_user_data, update_ticket_status) and demonstrates the model correctly selecting and formatting the arguments for these tools.

• Prompt Engineering for Structured Output

  Master the ability to force LLMs to return consistent, machine-readable formats. Exercise: Refine a system prompt to ensure an LLM consistently outputs JSON for intent classification, including specific fields for 'intent_category', 'confidence_score', and 'required_action'.

• Prepare a RAG system project walkthrough

  Develop a concise presentation of a RAG system you have built, focusing on retrieval quality, chunking strategies, and how you handled grounding or citations. Be prepared to explain how you evaluated the retrieval accuracy and addressed hallucinations.

• Demonstrate agentic workflow design

  Create a technical summary of an AI agent project that utilized tool calling or multi-step reasoning. Highlight how you managed state, handled API integrations, and implemented human-in-the-loop mechanisms for high-risk tasks.

• Develop an evaluation framework strategy

  Outline a systematic approach for monitoring production AI performance. Include specific metrics you would track for a customer service bot, such as latency, intent classification accuracy, and business-specific KPIs like task completion rates.

• Review CRM and API integration patterns

  Prepare to discuss your experience integrating AI services with external platforms like Zendesk or CRMs using FastAPI. Focus on how you handle authentication, JSON data structures, and robust error handling in production environments.

• Formulate questions for business stakeholders

  Prepare thoughtful questions regarding Hytech's current customer service pain points, the existing automation stack, and how the team balances rapid prototyping with the need for production-grade stability and security.