Large Language Model Architect

What matters most

• Translate strategy into an enterprise AI roadmap

  The role partners with CIOs, CTOs and business leaders, then leads assessments, target-state design, gap analysis and implementation sequencing. Be ready to explain how business outcomes, risk constraints and delivery dependencies shape a phased roadmap.

• Set Snowflake-native architecture standards

  The posting calls for Cortex-based agent, RAG, document intelligence and analytics architectures, Snowpark and Streamlit reference patterns, and standards for access control, masking, lineage, monitoring, cost and data security.

• Own cohesive, production-ready AI design

  This architect aligns domain specialists across LLM applications, multi-agent orchestration, context layers, inference and enterprise integration. The role also owns blueprints, architecture decision records, integration patterns and governance playbooks.

• Make AI quality and operational controls measurable

  The posting calls for evaluation and observability covering accuracy, relevance, groundedness, latency, cost, safety and reliability, alongside security, privacy, scalability and production-support standards.

Likely challenges to think through

• Balance Snowflake-native design with portability

  A likely design tension is using Cortex and Snowflake governance deeply while keeping the specified multi-agent architecture model- and tool-agnostic. Prepare a decision framework for what belongs inside Snowflake, what needs an abstraction boundary, and how each choice affects governance and operational complexity.

• Reconcile controls with inference speed and cost

  Regulated-workload protections and evaluation gates may compete with low-latency, high-throughput and cost targets. Prepare to walk through explicit non-functional targets and the trade-offs among model routing, caching, guardrails, monitoring and deployment patterns.

• Resolve decisions across stakeholders and domains

  Aligning executives, security teams, domain architects and delivery teams may require choosing between competing priorities. Prepare an example approach to documenting options, evidence, ownership and sequencing in an architecture decision record rather than treating alignment as a purely technical decision.

Topics to prepare

• Enterprise AI architecture and sequencing

  Review how to move from a current-state assessment to a target architecture: for a hypothetical regulated workload, identify gaps, define non-functional requirements, and sequence platform decisions. The posting explicitly calls for assessments, roadmaps, cross-domain alignment, and executive-level trade-offs.

• Snowflake-native AI architecture

  Map when Cortex Agents, Search, Analyst and AI Functions fit alongside Snowpark, Streamlit, Dynamic Tables, Tasks and Streams in a reference architecture. Explain the governance boundary and key trade-offs; the posting requires deep Snowflake architecture and hands-on expertise.

• RAG and enterprise context design

  Compare vector search, semantic retrieval, knowledge graphs and ontologies for a document-based question-answering scenario. Trace how retrieved evidence becomes prompt context and conversation state. These are explicit components of the role’s context-layer architecture.

• Multi-agent and inference decisions

  Sketch the responsibilities of orchestration, memory, tool authorization, an agent registry and an AI gateway; then justify routing, caching and latency choices. The posting calls for model- and tool-agnostic agents and production inference decisions.

• AI governance and evaluation

  Review how RBAC, masking, PII protection, lineage, guardrails and auditability connect to evaluation gates. Draft measures for groundedness, relevance, safety, latency and cost; all are named architecture controls or evaluation dimensions in the posting.

Skills to revise

• Build a Snowflake reference-pattern sketch

  Using a hypothetical regulated document workflow, draw data flow through Snowflake ingestion or processing, Cortex retrieval or analysis, and a Snowpark or Streamlit application; mark RBAC, masking, lineage and monitoring points. This rehearses the posting’s required Snowflake-native design work.

• Produce architecture decision records

  Write two short ADRs comparing RAG retrieval options and model-routing or caching options. State assumptions, alternatives, non-functional impacts and the decision rationale. The posting explicitly asks for defensible trade-offs and authoritative architecture artifacts.

• Design an evaluation and observability plan

  Create a small test matrix for an imagined RAG or agent workflow, with example checks for accuracy, groundedness, tool-use safety, latency and cost, plus a production monitoring response. The posting requires productized evaluation and observability practices.

• Present a phased architecture recommendation

  Practice a brief executive explanation of business outcomes, risks and implementation phases, followed by a delivery-team walkthrough of dependencies and controls. This targets the role’s explicit stakeholder communication and roadmap responsibilities.

• Build a requirements evidence sheet

  The posting lists enterprise architecture, engineering, LLM architecture and production LLM application experience, plus education requirements. Prepare a dated, factual evidence table for each; note that its 18-year minimum conflicts with the 12+ year qualification and 12–14 year header.

• Prepare an executive-level architecture walkthrough

  Choose a relevant enterprise AI example and prepare a short presentation showing the business objective, assessment, target state, gap analysis, phased roadmap and cross-domain decisions. Use a regulated-industry example if one is available.

• Draft a Snowflake-native reference blueprint

  As a practice exercise, diagram a RAG or agent application using relevant Cortex capabilities, Snowpark and Streamlit. Label data flows, integration boundaries, RBAC, masking, lineage, monitoring and cost controls; distinguish hands-on experience from proposed design.

• Work through a multi-agent design exercise

  Sketch orchestration, tool authorization, memory, an agent registry and an AI gateway, alongside retrieval and conversation-state choices. Prepare to explain when a simpler RAG design would be preferable.

• Create an evaluation and operations plan

  Prepare a sample scorecard for accuracy, relevance, groundedness, latency, cost, safety and reliability. Pair it with proposed evaluation gates, observability, PII protection and production-support measures.

• Write two defensible architecture decisions

  Draft brief ADRs comparing platform or model options and addressing trade-offs such as routing versus caching, or managed versus external components. Include assumptions, non-functional requirements, risks and the evidence that would change each decision.

• Bring focused questions for the employer

  Ask which experience figure governs this role, which Snowflake-native workloads are the immediate priority, and how architecture ownership is divided among domain architects, security teams and delivery teams.