Project Role : Large Language Model Architect

Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.

Must have skills : Large Language Models (LLMs)

Good to have skills : NA

Minimum 15 Year(s) Of Experience Is Required

Educational Qualification : 12 to 18 years of overall experience in enterprise technology delivery 5 years of experience in cloud solution or enterprise architecture

Summary

We are seeking a Senior Enterprise AI Architect to lead the strategy, architecture, and delivery of enterprise AI solutions using Gemini Enterprise, Google Cloud, Vertex AI, and agent platforms.

The architect will translate business priorities into secure, scalable, reliable, and measurable AI-enabled experiences, including enterprise search, knowledge assistants, copilots, and AI agents. The role will be responsible for solution architecture, technical governance, production readiness, adoption, and business value realization across complex enterprise environments.

Roles & Responsibilities:

Business and Solution Strategy

Lead discovery and architecture workshops to identify, assess enterprise AI use cases.

Translate business objectives into a solution roadmap, delivery scope, architecture, estimates, dependencies, risks, and measurable success criteria.

Advise stakeholders on when to use enterprise search, knowledge assistants, copilots, deterministic automation, AI agents, or a combination of these patterns.

Architecture and Platform Design

Design end-to-end enterprise AI architectures across Gemini Enterprise, enterprise search, connectors, retrieval services, custom agents, APIs, integration services, models, tools

Make architecture decisions regarding platform configuration, low-code extension, custom development, and integration with existing enterprise systems.

Define reference architectures, reusable patterns, architecture decision records, and technical standards for enterprise AI solutions.

Design for scalability, performance, reliability, maintainability, interoperability, & cost efficiency

Enterprise Knowledge and Retrieval

Define the enterprise knowledge architecture, including source-system onboarding, content ingestion, metadata, indexing, refresh, synchronization, retention, and deletion

Design retrieval-augmented generation patterns using semantic search, hybrid search, embeddings, reranking, grounding, and contextual retrieval.

Establish identity mapping, source-permission enforcement, document-level access controls,



and security trimming across enterprise knowledge sources.

Guide the use of structured data, unstructured content, semantic models, taxonomies, ontologies, and knowledge graphs where appropriate.

Define controls to maintain freshness, relevance, traceability, and source attribution for AI-generated responses.

Agent and Integration Architecture

Design enterprise AI agents and multi-agent solutions, including orchestration, tool/function calling, memory, planning, approvals, and human-in-the-loop controls.

Define secure integration patterns across enterprise applications, APIs, data sources, and business platforms, including identity, authorization, and delegated access controls.

Establish governance and guardrails for agentic solutions, covering security, risk management, approval workflows, safe automation, and responsible action execution

Responsible AI, Security, and Governance

Establish Responsible AI and governance standards covering privacy, security, safety, compliance, explainability, auditability, and human oversight.

Define security, risk, and control frameworks for enterprise AI solutions, including agent governance, prompt security, data protection, threat modelling, and regulatory compliance.

Implement monitoring, traceability, and audit mechanisms across AI interactions, retrieval systems, agent actions, tool execution, and business workflows.

Evaluation and Production Readiness

Define evaluation standards and production acceptance criteria for AI solution quality, safety, grounding, reliability, and effectiveness.

Establish production architecture standards covering scalability, performance, observability, security, cost, and operational readiness.

Implement LLMOps practices including evaluation, testing, prompt and model lifecycle management, CI/CD, release governance, and production monitoring

Engineering and Technical Leadership

Lead and mentor engineering teams through the end-to-end delivery of enterprise AI solutions, from ideation and prototyping through production deployment and support.

Provide architecture governance + hands-on tech leadership through design, code, security, infrastructure & deployment reviews, while developing reusable patterns and accelerators

Client and Delivery Leadership





Lead client architecture discussions and communicate complex technical decisions to business and technology stakeholders

Contribute to pre-sales activities, proposals, estimations, solution shaping, demonstrations, and architecture presentations

Manage architecture risks, dependencies, assumptions, non-functional requirements, and technical decision points

Skilled & Technical Skills:

Google Cloud and Enterprise AI

Strong Google Cloud architecture experience, with practical exposure to Gemini, Vertex AI, or equivalent enterprise AI capabilities.

Understanding of enterprise AI platform architecture, model consumption patterns, data boundaries, quotas, performance considerations, and cost management.

GenAI, Search, and Agentic AI

Gemini Enterprise, Google Agent space, Vertex AI, or equivalent enterprise AI platform experience.

Experience designing enterprise search and knowledge-assistant solutions

Understanding of prompt engineering, context management, model limitations, hallucination risks, evaluation approaches, and AI safety controls.

Enterprise Architecture and Integration

Strong enterprise architecture expertise across application, integration, data, security, identity, cloud, and AI domains, with experience producing solution blueprints and architecture governance artifacts.

Experience integrating enterprise platforms and data sources using APIs, connectors, event-driven architectures, data pipelines, and custom services.

Deep understanding of enterprise AI patterns including RAG, semantic search, enterprise search, grounding, retrieval, and agent architectures.

Ability to assess business requirements and determine when to apply GenAI, agentic AI, traditional automation, or custom solution approaches.

Preferred Experience

AI agent design and implementation, including multi-agent orchestration, MCP, A2A, ADK, and agent runtimes.

Enterprise search, knowledge management, taxonomy, ontology, and knowledge-graph solutions.

GenAI governance, evaluation, observability, safety, and Responsible AI frameworks.

Experience across leading enterprise AI ecosystems (e.g., Microsoft Copilot, Azure AI, Amazon Q, OpenAI, Anthropic) and relevant cloud/AI certifications.

Additional Information

- The candidate should have minimum 15 years of experience in Large Language Models (LLMs).
- This position is based at our Bengaluru office.
- A background of 12 to 18 years of overall experience in enterprise technology delivery and 5 years of experience in cloud solution or enterprise architecture is required.

📌 Large Language Model Architect (Bengaluru)
🏢 Accenture in India
📍 Bengaluru