Large Language Model Architect

What matters most

• Production-Grade AI Delivery

  The role demands moving beyond prototypes to deliver scalable, end-to-end GenAI solutions. You must demonstrate the ability to architect RAG pipelines, agentic workflows, and multi-model orchestration that are maintainable, reusable, and ready for enterprise environments.

• Technical Governance and Standards

  As a force multiplier, you are responsible for establishing engineering standards through Architecture Decision Records (ADRs) and design reviews. You must be able to translate complex AI strategies into executable technical roadmaps while ensuring the team adheres to high-quality engineering practices.

• Multi-Cloud Infrastructure and LLMOps

  You must design and manage AI infrastructure across AWS, Azure, or GCP using Kubernetes and IaC tools like Terraform. This includes embedding observability, token cost governance, and drift detection to ensure systems are performant and cost-effective.

• Responsible AI and Security

  A critical priority is implementing robust guardrails, including prompt injection defenses, PII redaction, and output filtering. You must ensure all AI solutions align with security standards and mitigate risks identified in the OWASP LLM Top 10.

Likely challenges to think through

• Balancing Innovation with Enterprise Constraints

  You will likely face the challenge of integrating cutting-edge LLM frameworks with rigid enterprise security and compliance requirements. Prepare by articulating how you balance rapid experimentation with the need for strict data residency, governance, and regulated industry standards.

• Multi-Cloud Complexity and FinOps

  Managing AI workloads across different cloud providers while maintaining cost visibility is a significant hurdle. Prepare by discussing your experience with FinOps, token cost optimization, and how you design infrastructure that remains portable and efficient across diverse cloud environments.

• Scaling AI Engineering Teams

  Acting as a force multiplier for a team of engineers requires balancing hands-on technical work with mentorship and leadership. Prepare to discuss your approach to code reviews, setting engineering bars, and guiding teams through the technical ambiguity inherent in GenAI development.

Topics to prepare

• Architecture Decision Records (ADRs) and Governance

  The role requires documenting technical trade-offs for enterprise AI systems. Exercise: Draft a sample ADR for choosing between a managed model-serving platform (e.g., Bedrock) versus a self-hosted Kubernetes-based deployment, focusing on cost, latency, and maintenance. This is relevant as the role demands establishing engineering standards and leading design reviews.

• Responsible AI and Security Frameworks

  Focus on mitigating risks like prompt injection and PII leakage. Exercise: Map the OWASP LLM Top 10 vulnerabilities to specific technical controls (e.g., input validation, output filtering, and data residency). This is essential for the role's requirement to implement guardrails and ensure compliance in regulated industries.

• LLMOps and Observability

  Understand the lifecycle of production AI systems. Exercise: Design a monitoring strategy using OpenTelemetry to track token usage, latency, and drift in a RAG pipeline. This is critical for the role's focus on building production-grade systems that require cost governance and performance tracking.

Skills to revise

• RAG Pipeline Design and Evaluation

  The role emphasizes moving beyond prototypes to production-grade RAG. Exercise: Build a small pipeline using LangChain or LlamaIndex and evaluate its retrieval accuracy using the RAGAS framework. This directly addresses the requirement for designing robust, scalable GenAI solutions.

• Infrastructure as Code (IaC) with Terraform

  The role requires multi-cloud delivery using IaC. Exercise: Write a Terraform module to provision a secure Kubernetes cluster (EKS, AKS, or GKE) with appropriate IAM roles and network policies. This is a core requirement for the infrastructure platform engineering aspect of the job.

• Fine-tuning and Model Optimization

  Focus on parameter-efficient techniques. Exercise: Create a workflow using PEFT and LoRA to fine-tune a small open-source model on a specific domain dataset. This is relevant as the role lists fine-tuning as an essential skill for customizing AI solutions.

• Prepare an Architecture Decision Record (ADR) sample

  Draft a sample ADR for a complex GenAI system, such as choosing between a vector database and a knowledge graph for a RAG pipeline. Focus on documenting trade-offs, cost implications, and scalability considerations to demonstrate your ability to lead technical governance.

• Develop a multi-cloud infrastructure strategy

  Create a high-level design document or diagram illustrating how you would deploy a production-grade LLM application across AWS and Azure using Terraform and Kubernetes. Include how you would handle cross-cloud networking, IAM, and consistent observability using OpenTelemetry.

• Build a RAG evaluation framework walkthrough

  Prepare a presentation on how you would implement quality gates for a RAG system. Detail your approach to using RAGAS or LLM-as-a-judge to measure retrieval accuracy and hallucination rates, and explain how you would integrate these into a CI/CD pipeline.

• Formulate a Responsible AI and Security plan

  Outline a strategy for mitigating OWASP LLM Top 10 risks. Prepare to discuss specific technical implementations for prompt injection defenses, PII redaction, and output filtering in a production environment.

• Prepare technical leadership case studies

  Identify two past projects where you acted as a force multiplier for a team of 10+ engineers. Be ready to explain how you mentored others, resolved technical conflicts, and translated complex business objectives into executable technical roadmaps.

• Draft strategic questions for the interviewers

  Prepare thoughtful questions regarding the team's current approach to FinOps for LLM token costs, their specific standards for model-serving infrastructure, and how they balance rapid GenAI experimentation with enterprise-grade security requirements.