Large Language Model Architect

What matters most

• Build production components in Snowflake

  The posting emphasizes hands-on engineering of Cortex-powered applications, including Cortex Search, Cortex AI Functions, Snowpark and Streamlit. Prepare to explain how an architecture becomes tested, deployable components inside Snowflake.

• Design and evaluate RAG and agent workflows

  The role covers ingestion, chunking, embeddings, retrieval, prompts, tools, orchestration and evaluation harnesses. Prepare to walk through design choices across that pipeline and how evaluation results inform changes.

• Engineer security and governance into AI applications

  The posting calls for RBAC, masking, PII redaction, prompt-injection defenses, authorization, lineage and audit logging, including for regulated use cases. Prepare to show where these controls belong in an application design.

• Lead maintainable client delivery

  The role combines coding and technical decisions with collaboration across architecture, data, product and security teams. It also calls for reusable patterns, integration specifications, runbooks and domain-aware design; prepare examples of those engineering outputs.

Likely challenges to think through

• Balance answer quality against operating cost

  Preparation hypothesis: retrieval depth, agent steps and model calls may improve results while increasing latency and cost. Prepare to discuss evaluation metrics and how you would use them alongside performance and cost controls named in the posting.

• Keep agent capabilities within security boundaries

  Preparation hypothesis: tool use and access to enterprise content can create tension between useful workflows and privacy or authorization controls. Prepare to reason through permission checks, PII handling, injection defenses and auditability at each step.

• Reuse patterns without ignoring domain constraints

  Preparation hypothesis: components built for different client domains may need different data handling, controls and adoption choices. Prepare to explain what you would standardize and what you would adapt after working with domain and security stakeholders.

Topics to prepare

• RAG architecture and retrieval quality

  Draw the path from enterprise documents through parsing, chunking, embeddings and vector search to a cited answer. Explain how you would test retrieval separately from answer generation. The posting explicitly calls for RAG pipelines and retrieval over structured and unstructured content.

• Agent orchestration and context handling

  Diagram a tool-calling workflow with agent responsibilities, context and memory boundaries, and failure paths. Explain when a direct model call would be simpler. The role includes prompts, tools, agents, orchestration flows and multi-agent workflows.

• Snowflake LLM architecture

  Map Cortex Search, Cortex AI Functions, Cortex Agents and Cortex Analyst to their possible roles in an application; then explain where Snowpark, Streamlit, Dynamic Tables, Tasks and Streams fit. The posting requires hands-on experience with these Snowflake capabilities.

• Security, privacy and governance

  Threat-model a regulated document assistant: trace PII exposure, prompt injection and unauthorized retrieval, then identify controls and audit evidence for each risk. The posting names RBAC, masking, guardrails, authentication, authorization, lineage and audit logging.

• Production evaluation and operations

  Draft an evaluation plan covering retrieval quality, answer quality, safety, latency and cost, with monitoring and regression checks after a change. The posting requires evaluation metrics, observability, reliability, scalability and cost controls.

Skills to revise

• Build a Python retrieval pipeline

  Using a small sample of structured and unstructured content, implement parsing, chunking and enrichment, then prepare embeddings and retrieval queries for Cortex Search. This rehearses the Python, ingestion and RAG implementation work explicitly described.

• Integrate a Snowflake application

  Prototype, or write a concrete implementation plan for, a Snowpark and Streamlit flow that invokes Cortex AI Functions and presents retrieved evidence. Identify where Cortex Agents or Cortex Analyst would be used rather than adding them without purpose. These integrations are named in the required skills and responsibilities.

• Implement controlled tool calling

  Write a small Python agent flow with a defined tool interface, context limits, error handling and a test for an unsafe or failed tool response. The role calls for hands-on agents, tool calling, orchestration and maintainable components.

• Apply data-access and safety controls

  Implement or work through a test case for RBAC, masking and PII redaction; include a prompt-injection attempt and verify that unauthorized content is neither retrieved nor returned. The posting specifically requires these production controls.

• Test and deliver an observable component

  Create unit and integration tests for one RAG component, add an evaluation regression check, and outline its Git-based CI/CD steps, logs and deployment runbook. The role requires automated testing, observability, CI/CD and engineering documentation.

• Map the stated requirements to evidence

  The posting specifies a minimum of 7.5 years, 15 years of full-time education, a relevant bachelor’s degree, Python, Snowflake and client-facing delivery. Prepare a factual evidence sheet for requirements you can document; leave gaps unclaimed.

• Prepare a production project walkthrough

  Select a real AI/ML or data product from one of the listed industries, if available. Summarize the problem, your hands-on contributions, architecture, controls, deployment and measured results; distinguish your work from the team’s.

• Sketch a Snowflake-based LLM design

  As interview practice, draw a RAG or agent workflow covering ingestion, parsing, chunking, embeddings, Cortex Search, Cortex AI Functions, tool calls and evaluation. Show where Snowpark or Streamlit fits, and label any design choices as proposals rather than job facts.

• Rehearse security and governance trade-offs

  The role calls for RBAC, masking, lineage, PII controls, prompt-injection defenses and audit logging. Prepare a threat-and-control table explaining where each applies in the proposed design and how you would test it.

• Define an evaluation and operations plan

  Draft a small evaluation matrix for retrieval, answer quality and agent behavior, including test cases and failure thresholds you would propose. Add monitoring, reliability, scalability and cost considerations; the posting names these concerns but prescribes no specific targets.

• Show how you turn designs into maintainable components

  Prepare a concise example of a Python API or component design with automated tests, Git/CI/CD steps, observability, an integration specification and a deployment runbook. Use actual artifacts where available; otherwise present a clearly labeled sample.

• Write questions for the employer

  Ask which Snowflake capabilities are in use, what enterprise data and controls shape the first engagement, how evaluation and production readiness are decided, and how the architect works with data engineers, product owners and security stakeholders.