Large Language Model Architect

What matters most

• Enterprise-Grade AI Architecture

  The primary focus is moving beyond prototypes to production-ready GenAI systems. You must demonstrate the ability to design scalable RAG pipelines, agentic workflows, and multi-model orchestration patterns while maintaining rigorous engineering standards through Architecture Decision Records (ADRs).

• Multi-Cloud Infrastructure and LLMOps

  You are expected to lead delivery across AWS, Azure, and GCP using Infrastructure-as-Code (Terraform/Pulumi) and Kubernetes. A critical priority is embedding observability, cost governance (FinOps), and LLMOps practices from the start to ensure systems are maintainable and cost-efficient.

• Responsible AI and Security Governance

  You must implement robust guardrails to mitigate risks such as prompt injection and OWASP LLM Top 10 vulnerabilities. This involves integrating output filtering, PII redaction, and data residency controls into the core architecture of all AI solutions.

Likely challenges to think through

• Balancing Technical Depth with Stakeholder Communication

  The role requires acting as a force multiplier who can write production-grade Python while simultaneously explaining complex architecture trade-offs to non-technical business stakeholders. Prepare by practicing how to translate high-level business objectives into specific, executable technical roadmaps without losing sight of architectural integrity.

• Managing Multi-Cloud Complexity

  Operating across AWS, Azure, and GCP simultaneously introduces significant overhead in IAM, networking, and service integration. Prepare by reviewing how to standardize deployment patterns using GitOps and IaC to ensure consistency across disparate cloud environments.

• Defining Evaluation and Quality Gates

  Moving from experimental AI to production requires establishing objective quality metrics. You will face the challenge of implementing evaluation frameworks (like RAGAS or LLM-as-judge) to ensure model performance meets enterprise standards. Prepare by articulating your approach to regression testing and quality gates for non-deterministic LLM outputs.

Topics to prepare

• Enterprise RAG and Agentic Architecture

  Focus on designing production-grade RAG pipelines and agentic workflows. Exercise: Draft an Architecture Decision Record (ADR) comparing different chunking strategies and orchestration patterns (e.g., LangChain vs. AutoGen) for a multi-tenant enterprise application. This is relevant to the role's requirement for designing scalable, non-prototype GenAI systems.

• LLMOps and Security Governance

  Study the OWASP LLM Top 10 and strategies for implementing guardrails. Exercise: Design a conceptual framework for integrating prompt injection defenses and PII redaction into an AI gateway layer. This directly addresses the requirement to implement responsible AI and security mitigations in production environments.

Skills to revise

• Multi-Cloud Infrastructure as Code (IaC)

  Demonstrate proficiency in Terraform for managing Kubernetes-based AI infrastructure. Exercise: Create a Terraform module that provisions a scalable model-serving endpoint on AWS or GCP, including necessary IAM roles and networking. This is essential for the role's focus on multi-cloud delivery and platform engineering.

• LLM Evaluation and Observability

  Gain hands-on experience with evaluation frameworks and telemetry. Exercise: Implement a basic RAGAS evaluation pipeline to measure faithfulness and answer relevance for a sample RAG system. This aligns with the requirement to embed observability and quality gates into AI delivery.

• Advanced Python for AI Systems

  Focus on production-grade code quality and system integration. Exercise: Refactor a prototype script into a modular, clean-architecture Python package that utilizes gRPC for inter-service communication. This is necessary for the role's emphasis on building maintainable, enterprise-grade AI solutions.

• Prepare an Architecture Decision Record (ADR) sample

  Draft a sample ADR for a complex GenAI system, such as choosing between a RAG-based approach versus fine-tuning for a specific enterprise use case. Focus on documenting trade-offs, cost implications, and scalability concerns.

• Develop a multi-cloud infrastructure strategy

  Create a high-level design document outlining how you would deploy a production-grade LLM application across AWS and Azure using Terraform and Kubernetes. Include your approach to managing model-serving infrastructure and AI gateways.

• Document a RAG pipeline and evaluation framework

  Prepare a technical walkthrough of a RAG pipeline you have built, specifically detailing your chunking strategy, vector store selection, and how you implemented evaluation metrics like RAGAS to ensure output quality.

• Outline an LLMOps and security governance plan

  Create a checklist for implementing OWASP LLM Top 10 mitigations and responsible AI guardrails, such as PII redaction and prompt injection defenses, within a CI/CD GitOps workflow.

• Formulate strategic questions for the interviewers

  Prepare questions regarding the team's current approach to FinOps for LLM token costs, how they handle multi-cloud data residency requirements, and the specific engineering standards they use for AI model governance.