Artificial intelligence Developer (Pro Code)

What matters most

• Production-Grade AI System Development

  The primary focus is building and owning AI solutions from prototype to production. You must demonstrate the ability to move beyond experimental code to scalable, reliable, and maintainable systems, ensuring performance, latency, and cost-efficiency are addressed as usage grows.

• Agentic Orchestration and RAG Implementation

  You are expected to design and build complex AI agents and RAG pipelines using Python. This requires proficiency in orchestration frameworks like LangChain, LangGraph, and AutoGen, alongside advanced RAG techniques such as hybrid search, re-ranking, and contextual chunking.

• Enterprise Integration and Cloud Deployment

  You must integrate AI capabilities into enterprise systems using APIs and the Model Context Protocol (MCP). The role requires hands-on cloud experience, specifically with Azure AI Foundry, to independently provision, deploy, and operate AI workloads.

• Rapid Prototyping and Iteration

  The role demands a strong bias for action, requiring you to translate loosely defined business problems into functional proofs-of-concept quickly. You must be comfortable iterating based on feedback and balancing the use of code-first development with low-code tools where appropriate.

Likely challenges to think through

• Balancing Speed with Production Reliability

  The requirement to rapidly prototype while maintaining production-grade standards creates a tension between shipping quickly and ensuring long-term system stability. Prepare by discussing your approach to technical debt, modular architecture, and how you implement automated testing and observability early in the development lifecycle.

• Managing Complexity in Agentic Workflows

  Orchestrating multi-agent systems and complex RAG pipelines can lead to unpredictable behavior and debugging difficulties. Prepare by explaining how you handle error propagation, state management in LangGraph, and how you use evaluation tools like LangSmith or Ragas to validate agent performance.

• Enterprise Security and Guardrail Integration

  Deploying AI in a large organization like WTW necessitates strict adherence to safety and compliance. Prepare by discussing your experience with implementing guardrails, content filtering, and grounding checks to ensure AI outputs remain secure and aligned with enterprise policies.

Topics to prepare

• Advanced RAG Architectures

  Review techniques beyond basic retrieval, specifically hybrid search, re-ranking, contextual chunking, and long-context strategies. Practice designing these pipelines to ensure high-quality, relevant LLM responses in enterprise environments.

• Agentic Orchestration Patterns

  Study design patterns for autonomous agents, including ReAct, plan-and-execute, and multi-agent systems. Focus on how these patterns manage task decomposition and state persistence in complex workflows.

• Production-Grade AI Engineering

  Focus on the lifecycle of AI solutions from prototype to production. Review concepts of observability, evaluation (using tools like ragas or LangSmith), error handling, and implementing guardrails for safety and grounding.

• Cloud-Native AI Deployment

  Understand how to deploy and scale AI workloads on cloud platforms like Azure AI Foundry. Focus on performance optimization, including latency management, throughput, and cost-effective scaling strategies.

Skills to revise

• Python for AI Development

  Demonstrate proficiency in writing clean, testable, and production-ready Python code. Practice building modular AI components that integrate seamlessly with enterprise APIs.

• LangChain and LangGraph Implementation

  Build a functional prototype using LangChain or LangGraph to orchestrate LLM calls. Focus on creating stateful agentic workflows that can handle multi-step reasoning tasks.

• Model Context Protocol (MCP) Integration

  Familiarize yourself with the Model Context Protocol to connect LLMs to external data sources and enterprise systems. Practice creating connectors that allow agents to interact with structured and unstructured data.

• Build a RAG pipeline with advanced techniques

  Develop a Python-based RAG system that demonstrates hybrid search, re-ranking, and contextual chunking. Be prepared to explain how you handle long-context strategies and optimize retrieval performance.

• Demonstrate agentic orchestration expertise

  Create a project using LangGraph or AutoGen that implements a multi-agent or ReAct pattern. Document how you manage state, agent communication, and error handling within the workflow.

• Prepare an Azure AI Foundry deployment walkthrough

  Outline your process for taking an AI prototype to production on Azure. Focus on how you manage cloud resources, scalability, latency, and cost optimization for enterprise-grade deployments.

• Develop an observability and evaluation strategy

  Prepare to discuss how you integrate tools like LangSmith or Ragas to monitor AI performance. Explain your approach to implementing guardrails and safety patterns for enterprise applications.

• Formulate technical questions for the hiring team

  Prepare questions regarding their current M365 integration challenges, how they prioritize Model Context Protocol (MCP) in their architecture, and their specific approach to balancing rapid prototyping with production reliability.