Large Language Model Architect
Accenture · Hyderabad · onsite · Posted 58d ago
Undisclosed10–12 yrsfulltimeSourced from
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About this role
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 12 year(s) of experience is required
Educational Qualification :15 years full time education
Summary:
Assesses whether proposed AI initiatives are viable across data availability, integration complexity, model readiness, security, latency, and cost - stress-tests assumptions early to prevent non-scalable pilots.
Description-
Technical Feasibility Lead specializing in Generative AI (GenAI) and Agentic AI to evaluate, validate, and de-risk AI initiatives before full-scale implementation. This role bridges innovation and execution by assessing technical viability, scalability, cost implications, integration complexity, and operational readiness of AI-driven solutions.
Conduct proof-of-concepts (PoCs) and pilot implementations.
Perform architecture validation and integration assessments.
Evaluate GenAI and Agentic AI use cases for technical viability and implementation risk.
Assess suitability of large language models such as OpenAI GPT models, Anthropic Claude, and Google DeepMind Gemini for business use cases.
Familiarity with vector databases and embedding pipelines.
Evaluate Retrieval-Augmented Generation (RAG), fine-tuning, and prompt engineering strategies.
Validate model performance using structured evaluation metrics.
Prototype agent orchestration using frameworks such as LangChain, AutoGPT, and Microsoft AutoGen.
Defining guardrails, human-in-the-loop mechanisms, and observability requirements.
Conduct adversarial testing and bias evaluation.
Experience in cost modeling and ROI analysis for AI initiatives.
Identify technical, operational, and security risks in proposed AI solutions.
Translate complex AI feasibility findings into executive-ready insights.
Provide go/no-go recommendations based on structured feasibility frameworks,Partner with Architecture, Engineering, Security, and Product teams.
Experience evaluating AI platforms across Amazon Web Services, Microsoft Azure, and Google Cloud.
Deep understanding of-
LLM architectures and transformers
RAG pipelines
Multi-agent systems
Cloud-native architecture
MLOps / LLMOps practices
Qualification
15 years full time education
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 12 year(s) of experience is required
Educational Qualification :15 years full time education
Summary:
Assesses whether proposed AI initiatives are viable across data availability, integration complexity, model readiness, security, latency, and cost - stress-tests assumptions early to prevent non-scalable pilots.
Description-
Technical Feasibility Lead specializing in Generative AI (GenAI) and Agentic AI to evaluate, validate, and de-risk AI initiatives before full-scale implementation. This role bridges innovation and execution by assessing technical viability, scalability, cost implications, integration complexity, and operational readiness of AI-driven solutions.
Conduct proof-of-concepts (PoCs) and pilot implementations.
Perform architecture validation and integration assessments.
Evaluate GenAI and Agentic AI use cases for technical viability and implementation risk.
Assess suitability of large language models such as OpenAI GPT models, Anthropic Claude, and Google DeepMind Gemini for business use cases.
Familiarity with vector databases and embedding pipelines.
Evaluate Retrieval-Augmented Generation (RAG), fine-tuning, and prompt engineering strategies.
Validate model performance using structured evaluation metrics.
Prototype agent orchestration using frameworks such as LangChain, AutoGPT, and Microsoft AutoGen.
Defining guardrails, human-in-the-loop mechanisms, and observability requirements.
Conduct adversarial testing and bias evaluation.
Experience in cost modeling and ROI analysis for AI initiatives.
Identify technical, operational, and security risks in proposed AI solutions.
Translate complex AI feasibility findings into executive-ready insights.
Provide go/no-go recommendations based on structured feasibility frameworks,Partner with Architecture, Engineering, Security, and Product teams.
Experience evaluating AI platforms across Amazon Web Services, Microsoft Azure, and Google Cloud.
Deep understanding of-
LLM architectures and transformers
RAG pipelines
Multi-agent systems
Cloud-native architecture
MLOps / LLMOps practices
Qualification
15 years full time education
Sourced from naukri
Skills
observabilityWeb Serviceslanguage modelsMLOpscloud nativeuse casesamazon web servicesgeminivector databasesHugging Face TransformersLarge Language Models (LLMs)cloudDesignArchitectureAi SolutionsMicrosoft AzureGenerative AiArtificial Intelligence
About Accenture
Accenture
IT Services & Consulting
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1989
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