Large Language Model Architect

What matters most

• Turn LLM architecture into working components

  The role owns hands-on design, coding, integration and testing of LLM applications, including RAG pipelines, agents, orchestration and evaluation harnesses. For preparation, be ready to explain how a design becomes tested, observable software rather than only a diagram.

• Build within the Snowflake platform

  The posting calls for Cortex-powered applications and agents, Cortex Search and AI Functions for RAG and document intelligence, and Streamlit/Snowpark integration. Prepare a concrete walkthrough of the platform components and their integration points.

• Engineer enterprise AI controls

  Duties include PII protection, prompt-injection defenses, guardrails, authentication, authorization, RBAC, masking, lineage and audit logging. Prepare to explain where these controls belong in an LLM workflow and how they would be tested.

• Lead delivery while contributing code

  This is a hands-on technical-lead role working with architects, data engineers, product owners and security stakeholders. It calls for reusable patterns, component designs, integration specifications, runbooks and evaluation results. Prepare an example of translating stakeholder constraints into an implementation and maintainable documentation.

Likely challenges to think through

• Retrieval quality across varied content

  Likely challenge, not a stated employer problem: parsing, chunking and retrieving both structured and unstructured enterprise content can improve coverage while introducing irrelevant or poorly grounded results. Prepare to explain a retrieval design, evaluation measures and how results would guide changes.

• Agent capability versus control

  Likely challenge, not a stated employer problem: tool-using, multi-agent workflows can add useful capabilities while expanding prompt-injection and authorization risks. Prepare to discuss tool boundaries, permission checks, guardrails and audit evidence without assuming unrestricted agent autonomy.

• Production performance versus cost and reliability

  Likely challenge, not a stated employer problem: Cortex-based applications must remain observable and scalable while managing latency and cost. Prepare to describe what you would measure, how you would test changes, and which design trade-offs you would revisit before deployment.

Topics to prepare

• LLM application architecture

  Source-backed: The role designs prompts, tool calls, agents, orchestration, context handling and RAG. Exercise: diagram a user request from model invocation through retrieval or tool use to response; mark where context is retained, access is checked and failures are handled.

• Snowflake Cortex architecture

  Source-backed: The posting calls for Cortex AI, Agents, Search, Analyst and AI Functions alongside Snowpark, Streamlit in Snowflake, Dynamic Tables, Tasks and Streams. Exercise: map these components to an enterprise document-question-answering design, explaining which handle retrieval, application logic, user interaction and data updates.

• Retrieval and evaluation design

  Source-backed: Ingestion, parsing, chunking, embeddings, vector search and evaluation harnesses are named responsibilities. Exercise: compare two chunking and retrieval approaches for mixed structured and unstructured content; define test questions and measures for answer quality and retrieval failures.

• Security and governance for production AI

  Source-backed: The role includes PII protection, prompt-injection defenses, RBAC, masking, lineage, authentication, authorization and audit logging. Exercise: threat-model a RAG request containing a malicious retrieved document and sensitive data; specify controls and evidence to inspect at each stage.

• Production architecture and delivery

  Source-backed: The posting emphasizes APIs, distributed systems, testing, CI/CD, observability, reliability and cost controls. Exercise: review a proposed LLM service design for failure modes, monitoring signals, deployment checks and cost drivers; record the trade-offs in a short component design.

• Industry-domain constraints

  Source-backed: The role asks for experience delivering AI/ML or data products in a listed industry and for attention to domain data, processes, controls and adoption risks. Exercise: choose one listed domain and identify which data restrictions and workflow constraints would change an otherwise generic RAG design; treat the scenario as preparation, not an assigned client project.

Skills to revise

• Build a Python retrieval pipeline

  Source-backed: Hands-on Python and ingestion-through-retrieval implementation are required. Exercise: write a small pipeline that parses sample documents, chunks and enriches them, creates embeddings and returns retrieved passages; add tests for empty, malformed and access-restricted inputs.

• Prototype a Cortex-backed application

  Source-backed: The role calls for Cortex Search and AI Functions, with Snowpark and Streamlit integration. Exercise: using a permitted sandbox if available, wire a small search-backed question-answering flow to a Snowpark or Streamlit interface; otherwise draft the integration code and interfaces without claiming execution.

• Implement and evaluate an agent workflow

  Source-backed: Agents, tool calling, orchestration and evaluation harnesses are explicit. Exercise: implement a bounded tool-calling flow with an allowlisted tool, a stop condition and test cases for correct routing, unsupported requests and tool failure; record the results.

• Test AI safety controls

  Source-backed: The posting specifies PII redaction, prompt-injection defenses, content filters and audit logging. Exercise: add checks to a sample request-and-retrieval flow, then run cases containing PII and malicious document instructions; verify redaction, rejection or safe handling, and logged outcomes.

• Prepare a production handoff

  Source-backed: Git-based development, automated testing, CI/CD, observability and deployment runbooks are expected. Exercise: package one practice component with automated tests, a CI/CD check, monitoring signals, an integration specification and a concise deployment-and-rollback runbook.

• Map the role to your evidence

  Prepare a one-page matrix linking the posting’s Python, cloud-native, GenAI, Snowflake and industry-domain requirements to specific examples you can discuss. Mark gaps honestly; the page lists a five-year minimum and additional typical experience profiles.

• Rehearse an end-to-end project walkthrough

  If you have a relevant project, prepare a diagram and brief account of your code contributions, architecture decisions, stakeholder collaboration and results. Focus on an LLM application, RAG pipeline or agent workflow, as described in the role.

• Sketch a Snowflake-based design

  As a practice exercise, diagram ingestion, parsing, chunking, embeddings and retrieval through Cortex Search and Cortex AI Functions, with a Snowpark or Streamlit interface. Be ready to explain where Cortex Agents or Analyst would—and would not—fit.

• Prepare a security and governance review

  Annotate the design with proposed RBAC, masking, PII redaction, prompt-injection defenses, authentication, audit logging and monitoring. Explain trade-offs rather than implying any one control makes an enterprise system safe.

• Show how you would test and operate it

  Draft a sample evaluation plan and deployment runbook covering retrieval and response quality, automated tests, CI/CD, observability, reliability and cost. These are preparation artifacts; the posting does not prescribe a particular test suite or target.

• Practice a technical-leadership example

  Prepare a concise account, if available, of turning a design into maintainable components with architects, data engineers, product owners or security stakeholders. Include a decision record or integration specification you could describe without revealing confidential information.

• Write targeted questions for the employer

  Ask which Snowflake capabilities are in active use, how teams evaluate LLM and agent behavior, what controls regulated engagements require, and how hands-on coding and technical leadership are divided in this role.