AI Infrastructure Architect

What matters most

• Executive AI strategy and investment sequencing

  The role partners with client executives to define target-state architecture and roadmaps, and leads assessments and platform comparisons. Prepare to explain how you would connect architecture choices and implementation sequence to business outcomes.

• End-to-end Azure AI architecture

  The architect owns cohesive designs across Azure AI platforms, LLM applications, agentic workflows, RAG, context services and enterprise integration. Prepare one clear reference-architecture walkthrough showing how these components fit together.

• Security, evaluation and operational governance

  The posting calls for identity and authorization controls, prompt-injection defense, PII protection, auditability, and production evaluation and observability. Prepare to show where controls sit in the architecture and how their effectiveness would be measured.

• Infrastructure and AI cost trade-offs

  The role explicitly covers infrastructure performance, power consumption, cost and scalability, as well as token budgets and cost monitoring. Prepare to explain how you would compare deployment options against non-functional requirements and usage costs.

• Architecture authority across teams

  The architect leads domain architects and senior specialists, makes defensible technology decisions, and stewards blueprints, ADRs and interface specifications. Prepare an example of how a decision becomes guidance that engineering teams can implement.

Likely challenges to think through

• Balancing autonomy with control

  Preparation hypothesis: Multi-agent orchestration and tool use may increase flexibility while expanding security and governance risk. Prepare to discuss boundaries for tool access, agent registration, certification gates and human oversight without assuming one control fits every use case.

• Grounding enterprise AI in usable data

  Preparation hypothesis: Knowledge graphs, vector search and reusable memory can improve context, but add complexity around relevance, permissions and data freshness. Prepare to compare context-layer options and explain how you would test groundedness and access controls.

• Choosing platforms under competing constraints

  Preparation hypothesis: Platform and vendor selection may require trading off model quality, latency, reliability, power consumption and cost. Prepare a decision framework with measurable criteria, evaluation evidence and an ADR-style rationale rather than a preferred-tool list.

Topics to prepare

• Enterprise AI architecture and sequencing

  The posting calls for target-state architecture, platform selection, reference architectures and investment roadmaps. Revision suggestion: outline how you would compare two platform approaches against business outcomes and sequence a transition from current state to target state.

• Azure generative AI platform design

  The role requires end-to-end Azure AI architecture, including Azure AI Foundry, Azure OpenAI, Azure AI Search and enterprise integration. Revision suggestion: trace a request from an application through retrieval, model inference and an enterprise API, explaining each component’s boundary.

• Agentic systems and enterprise context

  The posting specifies multi-agent orchestration, memory, tool use, registries, gateways, knowledge graphs, vector search and context assembly. Revision suggestion: compare when an agent should retrieve information, call a tool or retain conversational state, and identify the controls each choice needs.

• AI security and governance

  Required architectural controls include identity, authorization, private networking, prompt-injection defense, PII protection, audit logging and agent certification. Revision suggestion: threat-model a tool-using agent and map each trust boundary to a control and an audit record.

• Infrastructure trade-offs and AI operations

  The role emphasizes hardware and computational-stack performance, power, cost and scalability, alongside evaluation, observability and FinOps. Revision suggestion: work through a capacity decision using latency, reliability, token budgets and cost as competing constraints; state what you would measure before selecting an approach.

Skills to revise

• Draft an Azure solution blueprint

  The posting expects buildable blueprints, diagrams and interface specifications. Practice suggestion: draw a RAG-and-agent request flow using Azure AI Foundry, Azure OpenAI, Azure AI Search, API Management and Functions; label identity, data access and failure boundaries.

• Write an architecture decision record

  The role requires evidence-based technology choices and ADRs. Practice suggestion: write a short ADR comparing two retrieval or model-routing patterns, with decision criteria, non-functional trade-offs, rejected option and validation measures.

• Specify an evaluation and monitoring plan

  The posting calls for production evaluation and observability covering groundedness, safety, latency, reliability and cost. Practice suggestion: define a small test set, pass/fail criteria, production signals and an investigation path using Azure Monitor and Application Insights.

• Design agent access and cost controls

  The posting specifies Entra ID, Key Vault, AI gateways, usage labelling, token budgets and threshold alerts. Practice suggestion: sketch a tool-call authorization flow and a per-use-case budget policy, showing where credentials are protected, usage is recorded and over-budget calls are handled.

• Map the stated qualifications to evidence

  Prepare a one-page timeline and project index covering the listed experience thresholds, production LLM architecture, Azure work, industry context, and education. The posting specifies 15+ years overall and 15 years of full-time education.

• Build an executive-level transformation walkthrough

  Choose a relevant enterprise AI example and prepare a concise account of business goals, assessment findings, target architecture, investment sequencing, regulatory considerations, and measurable outcomes. Be ready to explain decisions made with business and technology leaders.

• Draw an end-to-end Azure AI architecture

  Prepare a diagram showing how Foundry or Azure OpenAI, AI Search, enterprise data, identity, private networking, APIs, and monitoring could work together for a grounded LLM application. Label assumptions and explain performance, reliability, and integration trade-offs.

• Work through an agent and context design

  Sketch an agentic workflow with orchestration, tool authorization, memory, retrieval, and a gateway or control plane. Show where knowledge graphs or vector search would fit, and justify the choices rather than treating every component as mandatory.

• Prepare a defensible infrastructure comparison

  Create a short decision record comparing plausible model, inference, and platform options against latency, scalability, power consumption, cost, and vendor-selection criteria. Include what measurements would validate the recommendation.

• Show how the platform would be controlled in production

  Bring a sample control and operations plan covering prompt-injection defenses, PII protection, auditability, evaluation of groundedness and safety, observability, incident support, and token budgets. Distinguish controls you have implemented from those you propose.

• Rehearse architecture leadership and employer questions

  Select an example of guiding domain architects through a blueprint, ADR, or governance decision. Prepare questions about the Chennai role’s architecture authority, current Azure platform, industry priorities, and how success is measured.