Custom Software Engineer

What matters most

• Agentic Workflow Orchestration

  The core requirement is to design and deploy autonomous agents capable of multi-step reasoning and planning. You must demonstrate proficiency in frameworks like LangGraph, CrewAI, or Google ADK to translate complex business workflows into functional, automated agent sequences.

• Production-Grade AI Engineering

  Moving beyond notebook-based prototyping, you must write robust, scalable Python or TypeScript code. This includes implementing state management, memory handling, and CI/CD practices to ensure agent services are reliable and maintainable in a production environment.

• Reliability and Observability

  Because agentic systems are non-deterministic, you must prioritize the implementation of guardrails, evaluation harnesses, and tracing tools like LangSmith or Langfuse to monitor performance, accuracy, and safety during execution.

• System Integration and RAG

  You are expected to bridge the gap between LLMs and enterprise data by building efficient RAG pipelines using vector databases and implementing tool-calling or MCP to allow agents to interact with external APIs and internal systems.

Likely challenges to think through

• Managing Non-Deterministic Outcomes

  Agentic systems often produce variable results, making traditional unit testing insufficient. Prepare by discussing how you design offline evaluation frameworks and human-in-the-loop review processes to validate agent logic and mitigate hallucinations.

• Balancing Latency, Cost, and Accuracy

  Integrating LLMs into business solutions requires navigating token economics and model constraints. You will face the challenge of selecting the right foundation model and architecture to meet performance requirements without exceeding budget or latency thresholds.

• State Management in Long-Running Tasks

  Maintaining context and state across multi-turn, complex agent interactions is technically difficult. Prepare to explain your approach to architecting memory systems that prevent context window overflow while ensuring the agent retains necessary information for multi-step tasks.

Topics to prepare

• Agentic Workflow Architecture

  Focus on the conceptual design of multi-step reasoning and planning systems. Practice mapping a complex business process into a sequence of agentic tasks, defining state management strategies, and handling long-running interactions. This is critical for the role's requirement to build autonomous systems that execute multi-step tasks.

• Evaluation and Observability for Non-Deterministic Systems

  Study methodologies for evaluating LLM-based applications, including offline evaluation harnesses, human-in-the-loop review, and A/B testing. Review how to implement tracing and logging to monitor agent reliability. This is essential for ensuring the production-grade quality requested in the job description.

Skills to revise

• Agent Framework Implementation

  Build a prototype using LangGraph or CrewAI that demonstrates tool-calling and function-calling capabilities. Ensure the agent can interact with an external API or database to solve a specific task. This directly addresses the requirement for hands-on experience with agent orchestration toolkits.

• Production-Grade Python Development

  Refactor a notebook-based LLM script into a modular, production-ready Python service. Incorporate asynchronous programming, robust error handling, and unit testing. This is necessary to meet the requirement for writing production-quality code rather than experimental research code.

• RAG Pipeline Construction

  Develop a retrieval-augmented generation pipeline using a vector database like Pinecone or Chroma. Focus on optimizing the retrieval process and managing context constraints. This is a core technical requirement for integrating external data into agentic workflows.

• Develop an Agentic Workflow Portfolio Project

  Build a functional prototype using LangGraph or CrewAI that demonstrates multi-step reasoning and tool-calling. Ensure the project includes a clear architecture diagram showing how the agent manages state, handles memory across turns, and interacts with an external API or vector database.

• Prepare a Production-Grade Code Walkthrough

  Select a piece of your previous production code (Python or TypeScript) that demonstrates robust error handling, asynchronous programming, and CI/CD practices. Be ready to explain how you managed LLM rate limits, token costs, and latency in a real-world deployment.

• Define an Evaluation and Observability Strategy

  Prepare to discuss how you measure agent reliability. Create a brief summary of how you would implement an evaluation harness (e.g., using LangSmith or Langfuse) to track traces, log agent decisions, and perform offline evaluations for non-deterministic AI outputs.

• Formulate Technical Questions for the Team

  Prepare questions regarding their current stack, such as: How does the team currently handle state persistence for long-running agents? What is the preferred approach for balancing model latency versus reasoning accuracy in their production business workflows?