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Large Language Model Architect

Accenture · Hyderabad, India

About this role

Accenture seeks a Large Language Model Architect in Hyderabad to design and deliver enterprise AI platforms on Snowflake. The role owns architectures for LLM applications, generative AI, RAG, agentic workflows and their integration with enterprise systems, drawing on experience in industries such as banking, insurance, retail, healthcare, travel, logistics or telecom.

Responsibilities

  • Translate business and product goals into architecture blueprints, non-functional requirements and implementation roadmaps; lead stakeholder workshops on feasibility, scope and delivery dependencies.
  • Design Snowflake-native Cortex agent, RAG, document intelligence and analytics solutions, including Snowpark and Streamlit application patterns and controls for access, masking, lineage, monitoring, cost and governance.
  • Architect multi-agent orchestration, tool use, memory and context management; design ingestion, preprocessing, chunking, embeddings, vector search, knowledge graphs and semantic retrieval.
  • Define evaluation for accuracy, relevance, faithfulness, groundedness, latency, cost, safety and reliability. Establish guardrails, prompt-injection defenses, PII protection, audit logging and tracing. Produce decision records, diagrams, specifications and reusable reference architectures.

Requirements

The posting states a minimum of 12 years of experience and 15 years of full-time education. Its detailed qualifications also specify a bachelor’s degree or equivalent in a related technical discipline; 10+ years in software, data or AI/ML engineering or technology architecture; 5+ years delivering advanced AI or cloud data solutions; 2+ years in agentic AI, LLM or generative AI architecture; and 4+ years each in Python coding and relevant ML, NLP, data engineering or AI delivery. Required experience includes Snowflake Cortex tools, Snowpark, Streamlit, Snowflake ML, security controls, LLM and RAG patterns, APIs, distributed systems, CI/CD, infrastructure as code, MLOps/LLMOps and stakeholder communication.

Preferred

SnowPro certifications or further Snowflake ecosystem experience; frameworks such as LangChain, LangGraph, LlamaIndex, Haystack and Semantic Kernel; and experience with responsible AI, model risk, red-teaming, human review, A/B testing, GenAI FinOps and reusable architecture assets.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Snowflake Data WarehouseSnowflake Cortex AICortex AgentsCortex SearchCortex AnalystCortex AI FunctionsSnowparkStreamlitDynamic TablesSnowflake TasksSnowflake StreamsSnowflake MLPythonLarge Language ModelsGenerative AIRetrieval-Augmented GenerationAgentic AIMulti-agent orchestrationEmbeddingsVector searchKnowledge graphsPrompt engineeringFine-tuningModel evaluationAI securityPrompt-injection defensePII protectionRBACMasking policiesOpenTelemetry tracing

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A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Turn business goals into architecture decisions

    The role translates product goals into blueprints, non-functional requirements and roadmaps, and leads workshops on scope and dependencies. Preparation suggestion: practice explaining a phased architecture and its trade-offs to product, engineering and security stakeholders.

  • Design Snowflake-native AI platforms

    The posting calls for Cortex-based agents, RAG, document intelligence and analytics within Snowflake, using Snowpark and Streamlit patterns. Preparation suggestion: be ready to explain component boundaries, enterprise integrations and why a given pattern fits.

  • Engineer reliable agent and retrieval foundations

    The architect designs multi-agent orchestration, tool use, memory and context management, plus ingestion, synchronization, chunking, embeddings and semantic retrieval. Preparation suggestion: walk through how data becomes usable, current context for an agent or RAG application.

  • Make evaluation and controls architectural requirements

    The role defines measures for groundedness, latency, cost, safety and reliability, and centrally enforced controls for access, PII, prompt injection, auditing and tracing. Preparation suggestion: show how these are designed and tested before production release.

  • Produce reusable, operable architecture assets

    Responsibilities include decision records, diagrams, specifications and reference architectures; required experience includes CI/CD, infrastructure as code, LLMOps and monitoring. Preparation suggestion: explain how a design can be deployed, observed and reused across implementations.

Likely challenges to think through

  • Snowflake-native design versus portability

    Likely preparation challenge, not a confirmed employer problem: the posting asks for Snowflake-native patterns and model- and tool-agnostic multi-agent systems. Prepare to explain which capabilities belong inside Snowflake, which warrant reusable interfaces, and the cost or complexity of each choice.

  • Retrieval quality versus freshness and cost

    Likely preparation challenge: synchronization, chunking and semantic retrieval must support reliable RAG while meeting latency and cost targets. Prepare a concrete approach to updating sources, measuring answer groundedness and diagnosing poor retrieval.

  • Agent autonomy versus enterprise control

    Likely preparation challenge: tool-using agents can expand what an application does while increasing security and operational risk. Prepare to discuss bounded tool permissions, prompt-injection defenses, human review where appropriate, tracing and evaluation before broader deployment.

03

Practice your answers

Technical practice

How would you turn a business goal into an enterprise AI architecture blueprint?

What to coverThe role calls for technical vision, non-functional requirements and an implementation roadmap. Suggested answer focus: define solution boundaries, integration dependencies and measurable requirements for security, latency, reliability and cost, then explain how you would document major decisions.

How would you design a Snowflake-native RAG application?

What to coverThe posting names Cortex-based RAG, Cortex Search, Snowpark and Streamlit in Snowflake. Suggested answer focus: trace a query from application entry through retrieval and generation, explaining component choices and where enterprise access controls apply.

How would you build and maintain the data and context layer for reliable retrieval?

What to coverThe role explicitly covers ingestion, preprocessing, synchronization, chunking, embeddings, vector search, knowledge graphs and semantic retrieval. Suggested answer focus: compare retrieval approaches and explain how you would detect stale, missing or poorly segmented source content.

When would you use an agentic workflow rather than a simpler LLM application?

What to coverThe posting calls for model- and tool-agnostic multi-agent design, including orchestration, tool use, memory and context management. Suggested answer focus: justify the added complexity, define tool permissions and handoffs, and address failure recovery and context limits.

How would you choose among prompting, retrieval, model routing and adaptation?

What to coverThese are LLM architecture patterns named in the posting, alongside function calling and tool integration. Suggested answer focus: compare their effects on grounding, latency, cost, maintainability and the need for domain-specific behavior.

How would you evaluate an LLM application before and after release?

What to coverThe role specifies accuracy, relevance, faithfulness, groundedness, latency, cost, safety, security and reliability. Suggested answer focus: describe representative test cases, measurable thresholds and regression checks, including how you would investigate a quality-versus-cost trade-off.

How would you protect a regulated GenAI workload against data exposure and prompt injection?

What to coverThe posting calls for RBAC, masking, PII protection, guardrails, access control and prompt-injection defenses. Suggested answer focus: describe controls across source data, retrieval, tools, prompts and outputs, plus how you would verify that those controls work.

How would you make a Snowflake-based AI system observable and governable?

What to coverThe role names lineage, monitoring, access history, audit logging, cost controls and OpenTelemetry-style tracing. Suggested answer focus: identify what to record across data, retrieval, model and tool steps, and how those signals would support troubleshooting without exposing sensitive content.

How would you release and operate reusable agent services in production?

What to coverThe posting specifies APIs, integration adapters, CI/CD, infrastructure as code, automated testing and MLOps/LLMOps. Suggested answer focus: explain service boundaries, deployment checks, rollback considerations and how you would monitor reliability after release.

How would you diagnose a RAG application that has become slow and expensive?

What to coverThe role emphasizes performance, scalability, cost optimization and operational support for Snowflake-native AI applications. Suggested answer focus: isolate retrieval, generation and orchestration costs and latency, then weigh changes against answer quality and governance requirements.

Behavioural practice

Tell me about a time you aligned stakeholders on an uncertain architecture scope.

What to coverThe posting calls for workshops covering feasibility, solution boundaries and delivery dependencies. Suggested answer focus: use a real situation, your actions to surface disagreements and clarify trade-offs, and the resulting decision or scope change.

Describe a time you communicated a technical risk to product, security and leadership stakeholders.

What to coverThe role requires recommendations across those groups. Suggested answer focus: explain the situation, how you tailored the evidence and options to each audience, and the result without claiming consensus if it did not occur.

Tell me about a time business processes or industry controls changed your solution design.

What to coverThe posting calls for domain-grounded architecture aligned with enterprise standards. Suggested answer focus: identify the real constraint, the design action you took and the outcome; use an example from your own experience rather than assuming a particular industry project.

Describe a production issue that led you to change an AI or data architecture.

What to coverThe role emphasizes operational reliability, monitoring and tracing. Suggested answer focus: cover the symptoms, how you investigated and coordinated the response, and what changed afterward to reduce recurrence.

Tell me about a time you made an architecture decision reusable for other teams.

What to coverThe posting calls for decision records, diagrams, specifications and reference architecture assets. Suggested answer focus: describe the original situation, how you documented and tested the pattern, and evidence of its later usefulness or limits.

02

Sharpen your knowledge

Topics to prepare

  • Enterprise AI architecture trade-offs

    Revision exercise: Turn a business goal into a component diagram, non-functional requirements and a phased roadmap; explain trade-offs in latency, reliability, security and cost. The posting assigns this architect ownership of blueprints, roadmaps and stakeholder decisions.

  • Snowflake-native AI architecture

    Revision exercise: Map where Cortex Agents, Search, Analyst and AI Functions, Snowpark and Streamlit would fit in a Snowflake-based application, and identify its security boundary. Snowflake Data Warehouse is the stated must-have skill, and these components appear in required experience.

  • RAG data and context design

    Revision exercise: Compare two designs for ingestion, synchronization, chunking, embeddings and semantic retrieval; explain how each affects freshness and groundedness. The role explicitly owns the end-to-end data and context layer.

  • Agent orchestration and integration

    Revision exercise: Draw a sequence diagram for an agent using an enterprise tool, including context management, memory and failure handling. Multi-agent orchestration, tool integration and reusable services are explicit responsibilities.

  • Evaluation, governance and operations

    Revision exercise: Define measures and release criteria for relevance, faithfulness, latency, cost and safety, then identify where access control, PII protection, audit logging and tracing apply. The posting calls for evaluation frameworks and centrally enforced controls.

Skills to revise

  • Design a Snowflake data workflow

    Revision exercise: Sketch a document-ingestion workflow using Streams, Tasks and Dynamic Tables, with RBAC, masking and access-history checks. The posting lists these Snowflake features and controls as required experience.

  • Prototype a Cortex-backed application pattern

    Revision exercise: Build or pseudocode a small Cortex Search retrieval flow with a Snowpark transformation and Streamlit interface; annotate where monitoring and cost controls belong. This rehearses the Snowflake-native application patterns named in the role.

  • Implement a Python agent integration

    Revision exercise: Write a small Python service that calls a tool through an API, bounds the context passed to an agent and handles timeouts and errors. Python, APIs, tool integration and reusable agent services are required experience.

  • Build an evaluation and security test set

    Revision exercise: Create sample grounded-answer, irrelevant-retrieval, prompt-injection and PII-exposure cases; specify expected outcomes and trace fields. This directly practices the posting’s evaluation, guardrail and observability responsibilities.

  • Present an architecture decision

    Revision exercise: Write a one-page decision record comparing two RAG or agent designs, then explain scope, dependencies and risks as if to product, security and engineering stakeholders. Decision records, diagrams and stakeholder workshops are explicit duties.

04

Your preparation checklist

  • Build a requirements evidence sheet

    Map the posting’s stated education and experience thresholds, Snowflake skills, Python delivery, and architecture work to specific projects, dates, artifacts and your contribution. Note that the posting states a 12-year minimum alongside more detailed experience thresholds; do not assume they are interchangeable.

  • Prepare an enterprise architecture walkthrough

    Choose a relevant industry project and bring a concise blueprint showing the business goal, non-functional requirements, enterprise integrations, key trade-offs and implementation roadmap. Explain how you aligned product, engineering and security stakeholders.

  • Whiteboard a Snowflake-native AI platform

    As a preparation exercise, draw a component and sequence diagram for a Cortex-based agent or RAG application. Show where Cortex Search, Snowpark and Streamlit fit, and explain when Cortex Analyst, AI Functions, Snowflake ML, Streams, Tasks or Dynamic Tables would be useful.

  • Work through a RAG and agent design exercise

    Sketch ingestion, preprocessing, chunking, embeddings, retrieval and context management for a domain-grounded use case. Add tool use, orchestration and memory where justified, and be ready to explain failure modes and alternatives.

  • Specify security and governance controls

    Prepare a control map for access, masking, PII protection, prompt-injection defense, lineage, audit logging and tracing within a Snowflake-based architecture. Include who enforces each control and what evidence it produces.

  • Draft an evaluation and release plan

    Create a sample evaluation table covering relevance, faithfulness, groundedness, latency, cost, safety and reliability. Describe how automated tests, CI/CD, infrastructure as code, monitoring and release checks would support the design.

  • Rehearse a stakeholder workshop

    Practice turning an ambiguous AI request into scope, feasibility assumptions, dependencies and architecture decisions. Bring a short decision record that explains options, risks and recommendations for product, engineering, security and leadership audiences.

  • Write focused questions for the employer

    Ask which Snowflake capabilities and enterprise integrations are in scope, which architecture domain the role would own, and how the team measures evaluation quality, governance and operating cost. Treat these as questions, not assumptions about the project.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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