AI Software Engineer III

What matters most

• Architecting Multi-Agent Orchestration

  The primary technical focus is designing and implementing complex agentic systems using LangGraph. You must demonstrate expertise in managing agent state, implementing checkpointing, and enabling human-in-the-loop workflows to ensure reliable, production-grade multi-agent collaboration.

• Establishing Engineering Standards

  As a senior engineer, you are expected to define and enforce best practices for the team. This includes creating reference implementations, setting standards for MCP server authoring, and ensuring consistent code quality across agentic patterns like planner/executor and supervisor models.

• Building Observability and Evaluation Stacks

  You must design systems to monitor agent performance, including tracing, token/cost telemetry, and failure-mode analysis. This requires integrating tools like LangSmith or Promptfoo to maintain high-quality RAG and agentic outputs in a multi-tenant SaaS environment.

Likely challenges to think through

• Balancing Multi-Tenant SaaS Constraints

  Deploying agentic systems within a multi-tenant SaaS architecture introduces complexities regarding security, identity propagation (Okta/OAuth2), and resource isolation. Prepare to discuss how you handle idempotency, retries, and queueing while maintaining strict data separation between tenants.

• Managing Agent Non-Determinism

  Production agentic systems often face challenges with non-deterministic behavior and complex error handling. Prepare by detailing your approach to building robust error contracts for MCP servers and how you use evaluation frameworks to validate agent reliability before deployment on Azure Foundry.

Topics to prepare

• Multi-Agent Orchestration Patterns

  Review architectural patterns such as planner/executor, supervisor, and reflection models. Exercise: Draft a system design document for a multi-agent workflow that handles a complex task by delegating sub-tasks to specialized agents, ensuring state persistence and human-in-the-loop checkpoints as required by the role.

• RAG System Architecture

  Focus on advanced retrieval strategies including hybrid search, reranking, and evaluation metrics. Exercise: Design a RAG pipeline that incorporates a reranking step and explain how you would measure retrieval quality using a framework like LangSmith or Promptfoo to ensure accuracy in a multi-tenant environment.

• Multi-tenant SaaS System Design

  Study principles of building scalable, secure SaaS platforms, specifically focusing on idempotency, retries, and queueing. Exercise: Outline a strategy for managing client-specific data isolation and API authentication using OAuth2/JWT within a multi-agent system to prevent cross-tenant data leakage.

Skills to revise

• LangGraph Implementation

  Practice building production-grade agents using LangGraph. Exercise: Create a small stateful graph that implements a 'human-in-the-loop' approval step for an agent action, ensuring the state is correctly checkpointed and recoverable.

• MCP Server Authoring

  Develop proficiency in creating Model Context Protocol servers. Exercise: Write a sample MCP server that exposes a tool with defined schemas, versioning, and error handling, ensuring it can be consumed by an LLM agent to perform a specific task.

• Agent Observability and Telemetry

  Gain hands-on experience with tracing and cost monitoring. Exercise: Configure a basic observability stack to track token usage, latency, and failure modes for a Python-based agent, ensuring that telemetry data is captured for debugging and performance analysis.

• Prepare a multi-agent system case study

  Document a past project where you implemented agentic patterns like supervisor or planner/executor using LangGraph. Be ready to explain your approach to state management, checkpointing, and human-in-the-loop integration.

• Review RAG architecture and evaluation strategies

  Prepare to discuss your experience with hybrid search, reranking, and retrieval strategies. Have a clear methodology for using frameworks like LangSmith or Promptfoo to evaluate RAG performance and observability.

• Master MCP server design principles

  Review the Model Context Protocol (MCP) specifications. Prepare to explain how you handle tool schemas, authentication, versioning, and error contracts when building robust MCP servers for production environments.

• Refine your system design for multi-tenant SaaS

  Prepare to discuss architectural patterns for multi-tenant SaaS, specifically focusing on idempotency, retries, queueing, and secure identity propagation using OAuth2/JWT within an Azure-based environment.

• Formulate technical leadership and mentorship examples

  Prepare specific anecdotes demonstrating your experience leading technical design reviews and mentoring junior engineers, highlighting how you establish and enforce engineering standards within a team.

• Develop targeted questions for the interviewers

  Prepare thoughtful questions regarding the Agora Orchestrator platform, the team's current challenges with Azure Foundry Agent Service, and how the company balances innovation with maintaining production-grade observability.