Gen AI Technical Lead-App Development

What matters most

• Architecting Agentic Workflows

  The role requires designing complex AI systems that move beyond simple chat interfaces to include planning, reasoning, state management, and memory. You must demonstrate how to orchestrate these components using frameworks like LangGraph or the Microsoft Agent Framework to ensure reliable, multi-step task execution.

• Enterprise RAG Implementation

  Building production-grade Retrieval-Augmented Generation (RAG) solutions is a core duty. This involves optimizing the entire pipeline, from vector database selection (PostgreSQL/pgvector, Cosmos DB) and retrieval logic to context enrichment and implementing AI guardrails for content safety.

• Cloud-Native Technical Leadership

  As a technical lead, you are responsible for the full software development lifecycle on Azure. This includes deploying microservices via Docker and AKS, implementing CI/CD pipelines, and ensuring security through managed identities, private endpoints, and robust monitoring.

Likely challenges to think through

• Balancing Agent Autonomy with Guardrails

  A significant challenge is ensuring that autonomous agents remain predictable and secure. Prepare by discussing how you implement AI guardrails and human-in-the-loop review processes to prevent hallucinations or unauthorized tool execution while maintaining the agent's reasoning capabilities.

• Optimizing Performance in Vector Search

  Enterprise RAG systems often face latency and accuracy issues at scale. Be prepared to discuss strategies for vector index optimization, caching, and batching to maintain performance, as well as how you approach model evaluation to measure the quality of retrieved context.

Topics to prepare

• Agentic AI Workflow Design

  Focus on the conceptual architecture of agentic systems, specifically planning, reasoning, and state management. Exercise: Draft a high-level design for an agent that uses tool calling to resolve a multi-step user query, ensuring you account for memory persistence and human-in-the-loop review points. This is critical as the role requires building production-grade agentic workflows.

• Enterprise RAG Architecture

  Review the components of a robust Retrieval-Augmented Generation pipeline, including context enrichment, retrieval logic, and AI guardrails. Exercise: Map out a data flow from a PostgreSQL/pgvector database through an Azure AI Search index to an LLM, identifying where you would implement content safety and performance optimization. This aligns with the requirement to build scalable RAG solutions.

• Cloud-Native AI Deployment

  Understand the integration of AI services within a containerized Azure environment. Exercise: Design a deployment strategy using Docker and Azure Kubernetes Service (AKS) that includes managed identities for secure access to Azure OpenAI and monitoring via Azure-native tools. This is relevant for the role's focus on secure, production-grade deployments.

Skills to revise

• LangGraph and Agent Orchestration

  Gain hands-on proficiency in building stateful, multi-actor applications. Exercise: Create a small LangGraph project that implements a simple 'supervisor' pattern where one agent routes tasks to specialized sub-agents. This directly addresses the requirement for experience with LangGraph and agent orchestration frameworks.

• Python API Development (FastAPI)

  Develop secure, high-performance microservices to serve AI models. Exercise: Build a FastAPI endpoint that accepts a prompt, interacts with an Azure OpenAI client, and returns a response with structured logging and error handling. This is essential for the role's requirement to develop Python-based APIs for enterprise applications.

• Vector Database Management (pgvector)

  Practice managing vector embeddings within relational databases. Exercise: Write a script to generate embeddings for a set of documents and perform a similarity search using pgvector in a PostgreSQL instance. This is a core technical requirement for the RAG solutions mentioned in the job description.

• Architect an Agentic Workflow Walkthrough

  Prepare a technical case study demonstrating an agentic workflow you designed. Focus on how you implemented planning, reasoning, state management, and tool calling using frameworks like LangGraph or Microsoft Agent Framework on Azure.

• Document Enterprise RAG Implementation Strategy

  Create a summary of your experience building RAG pipelines. Include how you handled vector search, context enrichment, and retrieval logic using Azure AI Search and PostgreSQL with pgvector, specifically addressing how you optimized for accuracy and latency.

• Prepare Evidence of AI Guardrails and Evaluation

  Compile examples of how you have implemented AI guardrails and model evaluation metrics. Be ready to discuss how you ensure content safety and reliability in production-grade GenAI applications.

• Outline a Cloud-Native Deployment Strategy

  Draft a high-level architecture diagram showing a Python-based microservice deployed on Azure Kubernetes Service (AKS). Include how you integrated CI/CD pipelines via Azure DevOps or GitHub Actions and managed security via Key Vault and private endpoints.

• Formulate Technical Leadership Questions

  Prepare questions for the interviewer regarding their current Agile delivery processes, how they balance rapid GenAI experimentation with production stability, and the specific challenges they face in scaling agentic workflows within their Azure environment.