AI Solutions Architect

Core Responsibilities

These are the recurring, ongoing accountabilities that define the role — not a project checklist, but the things an architect is responsible for continuously across every project that touches the AI platform.

A useful test for "is this an architect-level decision?"

Ask: if this decision is wrong, how expensive and how disruptive is it to reverse six months from now? A prompt template being wrong costs an afternoon. A vector database choice being wrong, once millions of production vectors and three downstream services depend on it, can cost months and real migration risk. The second kind of decision is where architect-level rigor — documented tradeoffs, a decision record, stakeholder sign-off — earns its cost in process overhead.

Reference Architecture Patterns

An architect needs a small set of reference patterns they can reach for, adapt, and defend — not a single "correct" diagram. These are the patterns that come up most often in both real design work and architect-level interviews.

Pattern 1 · Layered RAG platform

Client / App Layer
      |
API Gateway  ---->  Auth, rate limiting, request logging
      |
Orchestration Layer  ---->  LangGraph / custom router
      |            \
      |             ----> Tool-calling layer (MCP servers, internal APIs)
      |
Retrieval Layer  ---->  Vector store (pgvector/Pinecone) + hybrid keyword search
      |
Data Pipeline  ---->  Ingestion, chunking, embedding, CDC/incremental refresh
      |
Model Layer  ---->  Primary model + fallback model + local model (cost/latency tier)
      |
Observability  ---->  Tracing (LangSmith), eval (Ragas/DeepEval), drift (Evidently)

The architect-level judgment isn't drawing this diagram — most senior candidates can. It's being able to say precisely which layer breaks first under load, which layer is the expensive one to change later, and which layer needs to be swappable versus which can be tightly coupled.

Pattern 2 · Multi-model routing / fallback

Rather than a single model for every request, requests are routed by complexity, cost sensitivity, or latency requirement — a cheap/fast model handles simple classification or extraction, a frontier model handles complex reasoning, and a local/self-hosted model serves as both a cost lever and a fallback if an external provider has an outage.

Pattern 3 · Hybrid on-prem / cloud for regulated data

In healthcare and other regulated domains, a common architecture keeps PHI-bearing data and embeddings inside infrastructure the organization directly controls (self-hosted pgvector, on-prem or VPC-isolated compute), while routing de-identified or non-sensitive requests to managed cloud services for speed of iteration. The architect's job is defining exactly where that boundary sits and making sure it's enforced structurally (network isolation, data classification tagging) rather than by policy alone.

Pattern 4 · Agentic vs. fixed-pipeline topology

Covered in depth in the AI Systems guide from the analyst's implementation angle — from the architect's angle, the decision is about blast radius and predictability at the platform level: how many agentic subsystems does the platform allow before failure modes become impossible to reason about, and what guardrail budget (approval gates, spend caps, tool allow-lists) is mandatory before any team is allowed to ship an agent to production.

Cost & Performance Engineering

Analysts optimize a single query or a single pipeline. Architects own the cost curve of the entire platform — the difference between a system that's affordable at 1,000 requests/day and one that quietly becomes unaffordable at 1,000,000.

Cost levers, in the order an architect usually reaches for them

A framing that plays well in interviews

Executives don't want to hear "our token cost is $X" in isolation — they want "cost per resolved ticket," "cost per approved claim," or whatever the actual business unit of value is. An architect translates infrastructure cost into business-unit economics, and that translation is what justifies (or kills) a project at the budget-approval stage.

Governance, Security & Compliance

This is the area where an EDI/HIPAA integration background is a genuine, differentiated advantage — most AI-native architects have never had to think rigorously about audit trails, BAAs, or regulator-facing documentation the way healthcare integration work forces you to from day one.

The governance surfaces an architect owns

• Model risk management — documenting what each model in production is used for, its known failure modes, and how those failure modes are monitored (this mirrors model-risk practices from regulated finance, increasingly expected in healthcare AI too)
• Data governance — classification of what data can flow to which model/provider, especially PHI and other regulated categories; enforced structurally, not just by policy
• Vendor risk — signed BAAs before any PHI-adjacent workload touches a third-party model provider, verified against current documentation rather than assumed
• Audit trail design — can you reconstruct, after the fact, exactly what data went into a given model call and what came out, for any request from six months ago?
• Architecture review process — a lightweight but real gate (an architecture review board, or even a documented ADR + sign-off) before consequential platform decisions ship

Example Architecture Decision Record (ADR) structure

ADR-014: Vector store selection for clinical document retrieval

Status: Accepted
Context: Need to support semantic search over PHI-bearing clinical
  documents at ~2M vectors, growing ~15% per quarter. Compliance
  requires the data stay within infrastructure covered by our BAA.
Decision: Self-hosted pgvector inside existing VPC-isolated Postgres,
  not a managed third-party vector service.
Consequences:
  + No new BAA negotiation required; reuses existing Postgres audit
    tooling and backup/DR posture.
  + Team already has Postgres operational expertise.
  - We own index tuning and scaling ourselves past ~10M vectors,
    where a managed service might outperform with less operational
    burden — revisit this decision at that threshold.
Alternatives considered: Pinecone (rejected — BAA negotiation cost
  and timeline didn't fit this project's compliance deadline).

Interviewers who ask "walk me through an architecture decision you made" are really asking whether you can produce something with this shape — context, decision, consequences (including the honest downsides), and alternatives considered — from memory, on the spot.

Stakeholder & Cross-Functional Leadership

The architect is usually the person translating between three groups that don't naturally speak the same language: engineers who think in latency and API contracts, compliance/legal who think in risk and liability, and executives who think in cost and business outcomes. Being technically correct isn't enough if the decision can't be explained and defended to all three.

Common cross-functional friction points

Build vs. Buy Decision Framework

This is arguably the single most recurring decision an AI architect makes — and the single most common architect-level interview scenario. There's no universal right answer; there's a defensible framework for reasoning about it out loud.

A middle path architects reach for often: buy-then-abstract

Adopt a managed vendor now to move fast, but build a thin internal abstraction layer around it (an internal API that wraps the vendor SDK) so that swapping vendors later is a contained change rather than a rewrite touching every calling service. This is a very defensible answer in an interview because it acknowledges both the speed benefit of buying and the lock-in risk, and shows you design for optionality rather than betting the platform on a single vendor relationship.

Production-Level Interview Q&A