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Large Language Model Architect

Accenture

Chennai

12+ years

1 day ago

$63.9K–69.9K/yr

Full time

Onsite

Skills Required Gen AI

LLM Functions

RAG

Embeddings

Vector Database

Prompt Engineering

Fine-tuning

LLMOps

LangChain

LlamaIndex

Snowflake Data Warehouse

Machine Learning

Python

APIs

Distributed Systems

Description Senior AI/LLM architecture role focused on designing and delivering end-to-end enterprise AI platform architectures on Snowflake. The position spans generative AI, LLM applications, RAG, agentic workflows, governance, observability, and domain-grounded solution design.

Company: Accenture

Role: Large Language Model Architect

Location: Chennai

Experience

- Experience: 10-12 years
- Minimum 12 year(s) of experience is required
- Minimum 10+ years in software engineering, data engineering, AI/ML engineering or technology architecture
- Minimum 5+ years designing and deploying enterprise-grade advanced AI or cloud data solutions using at least one cloud vendor
- Minimum 2+ years in agentic AI, LLM and generative AI solution architecture or engineering delivery
- Minimum 4+ years of coding experience using Python
- Minimum 4+ years of experience in ML, deep learning, NLP, data engineering, analytical engineering or AI product delivery
- Demonstrated solution/technology architect experience in banking, insurance, retail, healthcare, travel, logistics or telecom

Qualification

- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline
- 15 years full time education

Responsibilities

- Design and deliver end-to-end AI platform architectures on Snowflake
- Own technical architecture for classical machine learning, generative AI, LLM applications, RAG, agentic workflows and enterprise AI platform integration
- Act as technical authority for agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, model platforms and inference
- Translate business strategy and product goals into technical vision, architecture blueprint,



non-functional requirements and implementation roadmap
- Lead stakeholder workshops to align on feasibility, scope, solution boundaries, delivery dependencies and client-facing expectations
- Define Snowflake-native AI application architecture including Cortex-based agents, RAG, document intelligence and analytics patterns
- Establish Snowpark and Streamlit application patterns
- Architect model- and tool-agnostic multi-agent systems with orchestration, tool use, agent memory, context management, MCP/control-plane patterns and reusable service abstractions
- Design the end-to-end data and context layer including ingestion, preprocessing, synchronization, chunking, embeddings, vector search, knowledge graphs and semantic retrieval
- Define evaluation frameworks for accuracy, relevance, faithfulness, groundedness, latency, cost, safety, security and operational reliability
- Maintain architecture decision records, component diagrams, sequence diagrams, design specifications, integration patterns and reusable reference architecture assets
- Communicate architecture trade-offs, risks and recommendations to engineering, product, security and leadership teams

Additional Responsibilities

- Define RBAC, masking, lineage, monitoring, cost controls and governance for regulated GenAI workloads within the Snowflake security perimeter
- Establish AI security, governance and observability as centrally enforced design controls including guardrails, prompt-injection defense, PII protection, access control, audit logging and OpenTelemetry-style tracing
- Design reusable agent services, memory services, API gateways, integration adapters, orchestration layers, evaluation harnesses and deployment pipelines
- Define enterprise AI platform patterns for performance, scalability, security, reliability, observability, governance,



cost optimization and operational support
- Work across regulated GenAI workloads
- Support client-facing expectations and delivery dependencies

Nice To Have

- Experience in Snowflake ML exposure
- SnowPro Advanced Architect certification
- SnowPro Advanced Data Engineer certification
- Snowpark Python
- Streamlit
- Semantic models
- dbt
- Native Apps
- Data sharing
- Cortex Guardrails
- Snowflake cost/performance tuning
- Responsible AI
- Model risk management
- AI governance boards
- Red-teaming
- Human-in-the-loop review
- A/B testing
- GenAI FinOps
- Reusable enterprise reference architectures
- Estimation models
- Accelerators
- Playbooks
- Architecture governance frameworks

More Skills Snowflake Cortex AI, Cortex Agents, Cortex Search, Cortex Analyst, Cortex AI Functions, Snowpark, Streamlit in Snowflake, Dynamic Tables, Tasks, Streams, Snowflake ML, RBAC, masking policies, access history, observability, model routing, adaptation, function calling, tool integration, agent orchestration, reusable frameworks, cloud-native application patterns, CI/CD, infrastructure-as-code, automated testing, model evaluation, MLOps, monitoring, production release governance, LangGraph, Haystack, Semantic Kernel, MLflow, FastAPI, Docker, Kubernetes, Snowpark Python, semantic models, dbt, Native Apps, data sharing, Cortex Guardrails, cost/performance tuning, OpenTelemetry

Prepare for this role

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