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 implement these using frameworks like LangGraph or Microsoft Agent Framework to ensure agents can execute multi-step tasks reliably.

• Enterprise-Grade RAG Implementation

  You are expected to build production-ready Retrieval-Augmented Generation solutions. This involves optimizing the entire pipeline, from vector search and embedding strategies using Azure AI Search and pgvector to ensuring data security and context enrichment for enterprise applications.

• Production-Grade Cloud-Native Engineering

  Beyond AI logic, you must ensure applications are scalable and secure. This includes managing Python microservices via FastAPI or Django, deploying on Azure Kubernetes Service (AKS), and implementing robust CI/CD pipelines with Azure DevOps or GitHub Actions.

• Technical Leadership and SDLC Governance

  As a lead, you are responsible for guiding development teams, performing code reviews, and ensuring engineering best practices. You must be prepared to handle technical estimation, root cause analysis, and cross-functional collaboration within an Agile environment.

Likely challenges to think through

• Balancing Agent Autonomy with Guardrails

  A significant challenge is ensuring that autonomous agents remain secure and predictable. Prepare to discuss how you implement AI guardrails, content safety, and human-in-the-loop review processes to prevent hallucinations or unauthorized tool execution in production.

• Optimizing Performance in Vector-Heavy Systems

  Scaling RAG and agentic systems often leads to latency issues. Be ready to explain your approach to performance tuning, such as vector index optimization, caching strategies, and asynchronous processing to maintain responsiveness under high load.

• Managing State in Distributed Agentic Systems

  Maintaining consistent state across distributed microservices and agentic workflows is complex. Prepare to discuss how you handle state management and memory persistence in a way that is both reliable and performant within an Azure-native architecture.

Topics to prepare

• Agentic AI Orchestration and State Management

  Focus on the conceptual design of agentic workflows, specifically how to implement planning, reasoning, and long-term memory within LLM applications. Exercise: Draft a high-level architecture diagram for an autonomous agent that requires multi-step tool calling and state persistence using LangGraph. This is critical for the role's requirement to build complex agentic workflows.

• Enterprise RAG Architecture and Guardrails

  Understand the end-to-end lifecycle of Retrieval-Augmented Generation, including embedding strategies, vector index optimization, and the implementation of AI guardrails for content safety. Exercise: Design a strategy for evaluating RAG retrieval accuracy and implementing a 'human-in-the-loop' review process. This aligns with the requirement to build secure, production-grade RAG solutions.

Skills to revise

• Azure AI and Cloud-Native Deployment

  Gain hands-on familiarity with deploying Python-based microservices to Azure Kubernetes Service (AKS) and integrating Azure OpenAI services. Exercise: Create a CI/CD pipeline configuration using GitHub Actions or Azure DevOps that automates the deployment of a FastAPI application to an Azure environment, including secret management via Key Vault.

• Vector Database Integration with Python

  Develop proficiency in querying and managing vector data using PostgreSQL with pgvector or Cosmos DB. Exercise: Write a Python script using FastAPI to perform a semantic search query against a vector database, demonstrating how to handle context enrichment and retrieval logic. This is a core requirement for the enterprise RAG solutions mentioned in the job description.

• LangGraph Framework Implementation

  Practice building stateful, multi-actor applications using LangGraph. Exercise: Build a simple graph-based workflow that demonstrates conditional branching and tool calling based on LLM output. This directly supports the requirement for expertise in Agentic AI frameworks.

• Architect an Agentic Workflow Walkthrough

  Prepare a technical case study demonstrating your experience with LangGraph or Microsoft Agent Framework. Focus on how you implemented planning, reasoning, and memory management in a production-grade agentic workflow, including how you handled tool calling and human-in-the-loop review.

• Demonstrate Enterprise RAG Implementation

  Create a summary of a RAG system you have built using Azure AI Search and vector databases like Cosmos DB or PostgreSQL with pgvector. Be prepared to explain your retrieval logic, context enrichment strategies, and how you optimized vector index performance.

• Prepare for Azure AI Security and Guardrails Discussion

  Review your experience implementing AI guardrails and content safety protocols. Prepare to discuss how you integrated Azure Key Vault, managed identities, and private endpoints to ensure secure, production-grade deployments of LLM-based microservices.

• Formulate Technical Leadership and Agile Scenarios

  Draft examples of how you have led technical design and estimation for AI projects. Focus on your ability to guide development teams through the SDLC, manage CI/CD pipelines using Azure DevOps or GitHub Actions, and perform root cause analysis for production issues.

• Develop Targeted Questions for the Interviewer

  Prepare questions regarding the team's current approach to model evaluation, the specific Azure AI Foundry services they prioritize, and how they balance rapid prototyping with the need for scalable, secure production deployments in their Agile environment.